diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 7bbef63..48ec9ba 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -7,7 +7,7 @@ on: branches: [ main, develop ] env: - PYTHON_VERSION: "3.12" + PYTHON_VERSION: "3.13" jobs: test: @@ -15,68 +15,71 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: ["3.12", "3.13"] - + python-version: ["3.13"] + steps: - name: Checkout code uses: actions/checkout@v4 - + - name: Install uv uses: astral-sh/setup-uv@v4 with: version: "latest" - + - name: Set up Python ${{ matrix.python-version }} run: uv python install ${{ matrix.python-version }} - + - name: Install dependencies run: | uv sync --all-extras --dev - + - name: Run linting run: | uv run ruff check src tests uv run ruff format --check src tests - + - name: Run type checking run: | uv run mypy src - + - name: Run tests run: | uv run pytest --cov=src/mcp_as_a_judge --cov-report=xml --cov-report=term-missing - + - name: Upload coverage to Codecov uses: codecov/codecov-action@v4 with: file: ./coverage.xml - fail_ci_if_error: true + fail_ci_if_error: false + verbose: true token: ${{ secrets.CODECOV_TOKEN }} + env: + CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} security: name: Security Scan runs-on: ubuntu-latest - + steps: - name: Checkout code uses: actions/checkout@v4 - + - name: Install uv uses: astral-sh/setup-uv@v4 with: version: "latest" - + - name: Set up Python run: uv python install ${{ env.PYTHON_VERSION }} - + - name: Install dependencies run: uv sync --all-extras --dev - + - name: Run safety check run: | uv add --dev safety uv run safety check - + - name: Run bandit security linter run: | uv add --dev bandit @@ -86,31 +89,31 @@ jobs: name: Build Package runs-on: ubuntu-latest needs: [test, security] - + steps: - name: Checkout code uses: actions/checkout@v4 - + - name: Install uv uses: astral-sh/setup-uv@v4 with: version: "latest" - + - name: Set up Python run: uv python install ${{ env.PYTHON_VERSION }} - + - name: Install dependencies run: uv sync --all-extras --dev - + - name: Build package run: | uv build --no-sources - + - name: Check package run: | uv add --dev twine uv run twine check dist/* - + - name: Upload build artifacts uses: actions/upload-artifact@v4 with: @@ -122,19 +125,21 @@ jobs: name: Build Docker Image runs-on: ubuntu-latest needs: [test, security] - + steps: - name: Checkout code uses: actions/checkout@v4 - + - name: Set up Docker Buildx uses: docker/setup-buildx-action@v3 - + - name: Build Docker image uses: docker/build-push-action@v5 with: context: . push: false tags: mcp-as-a-judge:test + build-args: | + VERSION=dev-${{ github.sha }} cache-from: type=gha cache-to: type=gha,mode=max diff --git a/.github/workflows/dependabot-auto-merge.yml b/.github/workflows/dependabot-auto-merge.yml index 69f5039..dfc17ea 100644 --- a/.github/workflows/dependabot-auto-merge.yml +++ b/.github/workflows/dependabot-auto-merge.yml @@ -18,7 +18,7 @@ jobs: uses: dependabot/fetch-metadata@v2 with: github-token: "${{ secrets.GITHUB_TOKEN }}" - + - name: Auto-merge Dependabot PRs for patch and minor updates if: steps.metadata.outputs.update-type == 'version-update:semver-patch' || steps.metadata.outputs.update-type == 'version-update:semver-minor' run: gh pr merge --auto --merge "$PR_URL" diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 33b3ea5..b2f0dab 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -6,7 +6,7 @@ on: - 'v*' env: - PYTHON_VERSION: "3.12" + PYTHON_VERSION: "3.13" REGISTRY: ghcr.io IMAGE_NAME: ${{ github.repository }} @@ -18,31 +18,31 @@ jobs: contents: write packages: write id-token: write - + steps: - name: Checkout code uses: actions/checkout@v4 with: fetch-depth: 0 - + - name: Install uv uses: astral-sh/setup-uv@v4 with: version: "latest" - + - name: Set up Python run: uv python install ${{ env.PYTHON_VERSION }} - + - name: Install dependencies run: uv sync --all-extras --dev - + - name: Extract version from tag id: version run: | VERSION=${GITHUB_REF#refs/tags/v} echo "VERSION=$VERSION" >> $GITHUB_OUTPUT echo "TAG=${GITHUB_REF#refs/tags/}" >> $GITHUB_OUTPUT - + - name: Verify version matches pyproject.toml run: | PROJECT_VERSION=$(uv run python -c "import tomllib; print(tomllib.load(open('pyproject.toml', 'rb'))['project']['version'])") @@ -50,7 +50,7 @@ jobs: echo "Version mismatch: tag=${{ steps.version.outputs.VERSION }}, pyproject.toml=$PROJECT_VERSION" exit 1 fi - + - name: Generate changelog id: changelog run: | @@ -60,23 +60,23 @@ jobs: git log --pretty=format:"- %s" $(git describe --tags --abbrev=0 HEAD^)..HEAD >> $GITHUB_OUTPUT || echo "- Initial release" >> $GITHUB_OUTPUT echo "" >> $GITHUB_OUTPUT echo "EOF" >> $GITHUB_OUTPUT - + - name: Build package run: | uv build --no-sources - + - name: Publish to PyPI uses: pypa/gh-action-pypi-publish@release/v1 with: password: ${{ secrets.PYPI_API_TOKEN }} - + - name: Log in to Container Registry uses: docker/login-action@v3 with: registry: ${{ env.REGISTRY }} username: ${{ github.actor }} password: ${{ secrets.GITHUB_TOKEN }} - + - name: Extract metadata for Docker id: meta uses: docker/metadata-action@v5 @@ -88,10 +88,10 @@ jobs: type=semver,pattern={{major}}.{{minor}} type=semver,pattern={{major}} type=raw,value=latest,enable={{is_default_branch}} - + - name: Set up Docker Buildx uses: docker/setup-buildx-action@v3 - + - name: Build and push Docker image uses: docker/build-push-action@v5 with: @@ -99,9 +99,11 @@ jobs: push: true tags: ${{ steps.meta.outputs.tags }} labels: ${{ steps.meta.outputs.labels }} + build-args: | + VERSION=${{ steps.version.outputs.VERSION }} cache-from: type=gha cache-to: type=gha,mode=max - + - name: Create GitHub Release uses: actions/create-release@v1 env: @@ -112,7 +114,7 @@ jobs: body: ${{ steps.changelog.outputs.CHANGELOG }} draft: false prerelease: false - + - name: Upload release assets uses: actions/upload-release-asset@v1 env: diff --git a/.github/workflows/semantic-release.yml b/.github/workflows/semantic-release.yml index bb9305a..f62e7f4 100644 --- a/.github/workflows/semantic-release.yml +++ b/.github/workflows/semantic-release.yml @@ -7,7 +7,7 @@ on: workflow_dispatch: env: - PYTHON_VERSION: "3.12" + PYTHON_VERSION: "3.13" jobs: semantic-release: @@ -19,34 +19,34 @@ jobs: issues: write pull-requests: write id-token: write - + steps: - name: Checkout code uses: actions/checkout@v4 with: fetch-depth: 0 token: ${{ secrets.GITHUB_TOKEN }} - + - name: Install uv uses: astral-sh/setup-uv@v4 with: version: "latest" - + - name: Set up Python run: uv python install ${{ env.PYTHON_VERSION }} - + - name: Install dependencies run: uv sync --all-extras --dev - + - name: Setup Node.js uses: actions/setup-node@v4 with: node-version: '20' - + - name: Install semantic-release run: | npm install -g semantic-release @semantic-release/changelog @semantic-release/git @semantic-release/github - + - name: Run semantic-release env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 7ac1ec6..a32c0da 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,4 +1,11 @@ repos: + # Security scanning with gitleaks - MUST be first for security + - repo: https://github.com/gitleaks/gitleaks + rev: v8.28.0 + hooks: + - id: gitleaks + + # General pre-commit hooks - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.6.0 hooks: @@ -11,7 +18,45 @@ repos: - id: check-merge-conflict - id: debug-statements - id: check-docstring-first + - id: check-case-conflict + - id: name-tests-test + args: ["--pytest-test-first"] + + # Python import sorting with isort + - repo: https://github.com/pycqa/isort + rev: 5.13.2 + hooks: + - id: isort + args: ["--profile", "black", "--line-length=88"] + + # Python code formatting with black + - repo: https://github.com/psf/black + rev: 24.8.0 + hooks: + - id: black + language_version: python3 + args: ["--line-length=88"] + + # Python linting with flake8 + - repo: https://github.com/pycqa/flake8 + rev: 7.1.1 + hooks: + - id: flake8 + args: + [ + "--max-line-length=88", + "--extend-ignore=E203,W503,E501", + "--max-complexity=10", + ] + additional_dependencies: + [ + flake8-docstrings, + flake8-bugbear, + flake8-comprehensions, + flake8-simplify, + ] + # Ruff for additional fast linting (complementary to flake8) - repo: https://github.com/astral-sh/ruff-pre-commit rev: v0.8.4 hooks: @@ -24,7 +69,9 @@ repos: hooks: - id: mypy additional_dependencies: [types-requests] - args: [--strict] + args: [--config-file, pyproject.toml, --ignore-missing-imports, src] + files: ^src/ + pass_filenames: false - repo: https://github.com/commitizen-tools/commitizen rev: v3.29.1 @@ -32,11 +79,17 @@ repos: - id: commitizen stages: [commit-msg] - - repo: https://github.com/python-poetry/poetry - rev: 1.8.4 + # Run tests with pytest + - repo: local hooks: - - id: poetry-check - files: pyproject.toml + - id: pytest + name: pytest + entry: pytest + language: system + types: [python] + pass_filenames: false + always_run: true + args: ["-v", "--tb=short"] - repo: https://github.com/pre-commit/mirrors-prettier rev: v4.0.0-alpha.8 @@ -51,8 +104,8 @@ ci: for more information, see https://pre-commit.ci autofix_prs: true - autoupdate_branch: '' - autoupdate_commit_msg: '[pre-commit.ci] pre-commit autoupdate' + autoupdate_branch: "" + autoupdate_commit_msg: "[pre-commit.ci] pre-commit autoupdate" autoupdate_schedule: weekly skip: [] submodules: false diff --git a/CHANGELOG.md b/CHANGELOG.md index 4e6b71a..fc9f7ba 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,6 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] ### Added + - Initial release infrastructure with CI/CD pipelines - Comprehensive GitHub Actions workflows for testing, building, and releasing - Semantic versioning with automated releases @@ -17,18 +18,22 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - Dependabot configuration for automated dependency updates ### Changed + - Updated project configuration for better packaging and CI/CD integration - Modernized tooling configuration (ruff, mypy, pytest) ### Fixed + - N/A ### Removed + - N/A ## [0.1.0] - TBD ### Added + - Initial release of MCP as a Judge - Core MCP server functionality - AI-powered code evaluation tools diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 077368c..6eccded 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -9,7 +9,8 @@ MCP as a Judge aims to revolutionize software development by preventing bad codi ## ๐Ÿš€ **Getting Started** ### **Prerequisites** -- Python 3.12.10+ (latest secure version) + +- Python 3.13.5+ (latest secure version) - uv (recommended) or pip - Git - A compatible MCP client for testing @@ -17,12 +18,14 @@ MCP as a Judge aims to revolutionize software development by preventing bad codi ### **Development Setup** 1. **Fork and clone the repository:** + ```bash git clone https://github.com/hepivax/mcp-as-a-judge.git cd mcp-as-a-judge ``` 2. **Set up development environment:** + ```bash # Install uv if you don't have it pip install uv @@ -34,11 +37,13 @@ uv pip install -e ".[dev]" ``` 3. **Install pre-commit hooks:** + ```bash pre-commit install ``` 4. **Verify setup:** + ```bash # Run tests uv run pytest @@ -52,18 +57,21 @@ uv run mypy src ## ๐Ÿ“ **Development Guidelines** ### **Code Style** + - Follow PEP 8 and use Black for formatting - Use type hints for all function parameters and return values - Write comprehensive docstrings for all public functions and classes - Keep line length to 88 characters (Black default) ### **Testing** + - Write tests for all new functionality - Maintain test coverage above 80% - Use descriptive test names that explain what is being tested - Include both unit tests and integration tests ### **Documentation** + - Update README.md for user-facing changes - Add docstrings to all new functions and classes - Update type hints and model schemas @@ -72,24 +80,28 @@ uv run mypy src ## ๐Ÿ”ง **Types of Contributions** ### **๐Ÿ› Bug Fixes** + - Check existing issues before creating new ones - Include steps to reproduce the bug - Add tests that verify the fix - Update documentation if needed ### **โœจ New Features** + - Discuss major features in an issue first - Ensure features align with project vision - Include comprehensive tests - Update documentation and examples ### **๐Ÿ“š Documentation** + - Fix typos and improve clarity - Add examples and use cases - Improve setup instructions - Translate documentation (if applicable) ### **๐Ÿงช Testing** + - Add missing test coverage - Improve test quality and reliability - Add integration tests @@ -98,6 +110,7 @@ uv run mypy src ## ๐Ÿ”„ **Development Workflow** ### **1. Create a Branch** + ```bash git checkout -b feature/your-feature-name # or @@ -105,12 +118,14 @@ git checkout -b fix/bug-description ``` ### **2. Make Changes** + - Write code following the style guidelines - Add tests for your changes - Update documentation as needed - Run tests locally to ensure everything works ### **3. Quality Checks** + ```bash # Format code uv run black src tests @@ -129,12 +144,14 @@ uv run pytest --cov=src/mcp_as_a_judge ``` ### **4. Commit Changes** + ```bash git add . git commit -m "feat: add user requirements alignment to judge tools" ``` **Commit Message Format:** + - `feat:` for new features - `fix:` for bug fixes - `docs:` for documentation changes @@ -144,11 +161,13 @@ git commit -m "feat: add user requirements alignment to judge tools" - `chore:` for maintenance tasks ### **5. Push and Create PR** + ```bash git push origin your-branch-name ``` Then create a Pull Request on GitHub with: + - Clear description of changes - Link to related issues - Screenshots/examples if applicable @@ -157,6 +176,7 @@ Then create a Pull Request on GitHub with: ## ๐Ÿงช **Testing Guidelines** ### **Running Tests** + ```bash # Run all tests uv run pytest @@ -172,6 +192,7 @@ uv run pytest -m "not slow" ``` ### **Writing Tests** + - Use descriptive test names: `test_judge_coding_plan_with_user_requirements` - Test both success and failure cases - Mock external dependencies @@ -194,17 +215,20 @@ Before submitting a PR, ensure: ## ๐Ÿšจ **Important Guidelines** ### **User Requirements Focus** + - All judge tools must consider user requirements alignment - New features should enhance user-driven decision making - Avoid hidden fallbacks - always involve users in critical decisions ### **Quality Standards** + - Maintain high code quality standards - Ensure comprehensive error handling - Follow software engineering best practices - Write maintainable, readable code ### **Backward Compatibility** + - Avoid breaking changes when possible - Deprecate features before removing them - Provide migration guides for breaking changes @@ -228,6 +252,7 @@ Before submitting a PR, ensure: ## ๐ŸŽ‰ **Recognition** Contributors will be recognized in: + - README.md contributors section - Release notes for significant contributions - GitHub contributor graphs diff --git a/Dockerfile b/Dockerfile index 32be7ba..60961ae 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,9 +1,8 @@ # Multi-stage build for production-ready MCP as a Judge server -FROM python:3.12-slim AS builder +FROM python:3.13-slim AS builder # Set build arguments -ARG PORT=8050 -ARG TRANSPORT=sse +ARG VERSION=latest # Set environment variables ENV PYTHONUNBUFFERED=1 \ @@ -34,18 +33,15 @@ RUN .venv/bin/pip install uv RUN .venv/bin/uv pip install -e . # Production stage -FROM python:3.12-slim AS production +FROM python:3.13-slim AS production # Set build arguments -ARG PORT=8050 -ARG TRANSPORT=sse +ARG VERSION=latest # Set environment variables ENV PYTHONUNBUFFERED=1 \ PYTHONDONTWRITEBYTECODE=1 \ - PATH="/app/.venv/bin:$PATH" \ - PORT=${PORT} \ - TRANSPORT=${TRANSPORT} + PATH="/app/.venv/bin:$PATH" # Install runtime dependencies only RUN apt-get update && apt-get install -y \ @@ -72,12 +68,9 @@ RUN chown -R mcpuser:mcpuser /app # Switch to non-root user USER mcpuser -# Expose port -EXPOSE ${PORT} - -# Health check +# Health check for MCP server (check if process is running) HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \ - CMD curl -f http://localhost:${PORT}/health || exit 1 + CMD pgrep -f "mcp-as-a-judge" || exit 1 # Default command CMD ["mcp-as-a-judge"] @@ -85,7 +78,7 @@ CMD ["mcp-as-a-judge"] # Labels for metadata LABEL org.opencontainers.image.title="MCP as a Judge" \ org.opencontainers.image.description="AI-powered code evaluation and software engineering best practices enforcement" \ - org.opencontainers.image.version="1.0.0" \ + org.opencontainers.image.version="${VERSION}" \ org.opencontainers.image.authors="Zvi Fried" \ org.opencontainers.image.source="https://github.com/hepivax/mcp-as-a-judge" \ org.opencontainers.image.licenses="MIT" diff --git a/LICENSE b/LICENSE index 56a618e..68e34ac 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2024 MCP as a Judge Contributors +Copyright (c) 2025 MCP as a Judge Contributors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/PROJECT_SUMMARY.md b/PROJECT_SUMMARY.md deleted file mode 100644 index 94e20f7..0000000 --- a/PROJECT_SUMMARY.md +++ /dev/null @@ -1,121 +0,0 @@ -# MCP as a Judge - Project Summary - -## โœ… Project Completed Successfully - -I have successfully created the "MCP as a Judge" project as requested. Here's what was built: - -## ๐ŸŽฏ Project Overview - -A Model Context Protocol (MCP) server that acts as a software engineering judge to validate coding plans and code changes against best practices. - -## ๐Ÿ› ๏ธ Technical Implementation - -### Core Components - -1. **MCP Server** (`src/mcp_as_a_judge/__init__.py`) - - Built using FastMCP from the official MCP Python SDK - - Uses Python 3.12 with uv for dependency management - - Implements MCP sampling to query the client LLM for judgments - -2. **Two Mandatory Judge Tools**: - - **`judge_coding_plan`** - - Description: "You MUST call this tool prior making any code change to validate your coding plan follows SWE best practices." - - **Requires**: plan, design, research (all mandatory parameters) - - Forces agents to provide comprehensive system design and thorough research - - Validates against design quality, research thoroughness, architecture, security, testing, and maintainability - - Uses LLM sampling for intelligent analysis - - **`judge_code_change`** - - Description: "You MUST call this tool prior making any code change to validate the implementation follows SWE best practices." - - Reviews actual code changes for quality, security, performance, error handling, and maintainability - - Provides structured feedback with approval/revision status - -### Key Features Implemented - -โœ… **Mandatory Usage Enforcement**: Both tools have descriptions that force the host to use them -โœ… **LLM Sampling**: Uses MCP sampling to leverage the client LLM for expert-level analysis -โœ… **Comprehensive Review Criteria**: Covers all major SWE best practices -โœ… **Structured Output**: Returns formatted responses with status and recommendations -โœ… **Latest MCP SDK**: Uses mcp[cli] latest version as requested -โœ… **Python 3.12 + uv**: Modern Python setup with uv package management - -## ๐Ÿ“ Project Structure - -``` -mcp-as-a-judge/ -โ”œโ”€โ”€ src/mcp_as_a_judge/ -โ”‚ โ””โ”€โ”€ __init__.py # Main MCP server with judge tools -โ”œโ”€โ”€ tests/ -โ”‚ โ”œโ”€โ”€ test_server.py # Basic server tests -โ”‚ โ””โ”€โ”€ test_server_startup.py # Server startup validation -โ”œโ”€โ”€ README.md # Comprehensive documentation -โ”œโ”€โ”€ example_usage.py # Usage examples -โ”œโ”€โ”€ mcp_config_example.json # MCP client configuration example -โ”œโ”€โ”€ pyproject.toml # Project configuration -โ””โ”€โ”€ PROJECT_SUMMARY.md # This summary -``` - -## ๐Ÿš€ Usage - -### Installation -```bash -cd mcp-as-a-judge -uv sync -``` - -### Running -The server is designed to be used via MCP clients (Claude Desktop, Cline, etc.): - -```json -{ - "mcpServers": { - "mcp-as-a-judge": { - "command": "uv", - "args": ["run", "mcp-as-a-judge"], - "cwd": "/path/to/mcp-as-a-judge" - } - } -} -``` - -## ๐Ÿ” Review Criteria - -### For Coding Plans: -- Architecture & Design (SOLID principles, modularity) -- Security (vulnerabilities, best practices) -- Code Reuse & Dependencies (library usage, avoiding reinvention) -- Testing Strategy (approach, edge cases) -- Performance & Scalability -- Maintainability (structure, documentation) - -### For Code Changes: -- Code Quality (cleanliness, conventions) -- Security (vulnerabilities, input validation) -- Performance (algorithm choice, efficiency) -- Error Handling (comprehensive coverage) -- Testing (testability, coverage) -- Dependencies & Reuse -- Maintainability (patterns, documentation) - -## โœ… Testing - -All tests pass: -- Server initialization โœ“ -- Tool registration โœ“ -- Import functionality โœ“ -- Package entry point โœ“ - -## ๐ŸŽ‰ Mission Accomplished - -The project fully meets all requirements: -- โœ… Python with uv -- โœ… MCP[cli] latest version -- โœ… FastMCP server implementation -- โœ… Two judge tools with mandatory descriptions -- โœ… LLM sampling for intelligent analysis -- โœ… SWE best practices validation -- โœ… Comprehensive documentation -- โœ… Working test suite - -The server is ready to be integrated with MCP clients and will enforce software engineering best practices by requiring validation before any code changes! diff --git a/README.md b/README.md index fcd8b95..40b0e20 100644 --- a/README.md +++ b/README.md @@ -1,436 +1,230 @@ -# ๐Ÿšจ MCP as a Judge +# โš–๏ธ MCP as a Judge > **Prevent bad coding practices with AI-powered evaluation and user-driven decision making** [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) -[![Python 3.12+](https://img.shields.io/badge/python-3.12+-blue.svg)](https://www.python.org/downloads/) +[![Python 3.13+](https://img.shields.io/badge/python-3.13+-blue.svg)](https://www.python.org/downloads/) [![MCP Compatible](https://img.shields.io/badge/MCP-Compatible-green.svg)](https://modelcontextprotocol.io/) -[![Docker](https://img.shields.io/badge/docker-supported-blue.svg)](https://www.docker.com/) [![CI](https://github.com/hepivax/mcp-as-a-judge/workflows/CI/badge.svg)](https://github.com/hepivax/mcp-as-a-judge/actions/workflows/ci.yml) [![Release](https://github.com/hepivax/mcp-as-a-judge/workflows/Release/badge.svg)](https://github.com/hepivax/mcp-as-a-judge/actions/workflows/release.yml) [![PyPI version](https://badge.fury.io/py/mcp-as-a-judge.svg)](https://badge.fury.io/py/mcp-as-a-judge) -[![Docker Image](https://ghcr-badge.egpl.dev/hepivax/mcp-as-a-judge/latest_tag?trim=major&label=latest)](https://github.com/hepivax/mcp-as-a-judge/pkgs/container/mcp-as-a-judge) + [![codecov](https://codecov.io/gh/hepivax/mcp-as-a-judge/branch/main/graph/badge.svg)](https://codecov.io/gh/hepivax/mcp-as-a-judge) -**MCP as a Judge** is a revolutionary Model Context Protocol (MCP) server that acts as an intelligent gatekeeper for software development. It prevents bad coding practices by using AI-powered evaluation and involving users in critical decisions when requirements are unclear or obstacles arise. +**MCP as a Judge** is a revolutionary Model Context Protocol (MCP) server that **transforms the developer-AI collaboration experience**. It acts as an intelligent gatekeeper for software development, preventing bad coding practices by using AI-powered evaluation and involving users in critical decisions when requirements are unclear or obstacles arise. + +> **โš–๏ธ Concept**: This project extends the **LLM-as-a-Judge** paradigm to software engineering workflows, where AI models evaluate and guide development decisions rather than just generating code. + +## โš–๏ธ **Main Purpose: Improve Developer-AI Interface** -## ๐ŸŽฏ **This MCP Will Change Many Developers' Lives!** +**The core mission is to enhance the interface between developers and AI coding assistants** by: -### **What It Prevents:** -- โŒ Reinventing the wheel instead of using existing solutions -- โŒ Building workarounds instead of proper implementations -- โŒ Insufficient research leading to poor architectural decisions -- โŒ Misalignment between code and user requirements -- โŒ Deployment of problematic code without proper review +- ๐Ÿ›ก๏ธ **Preventing AI from making poor decisions** through intelligent evaluation +- ๐Ÿค **Involving humans in critical choices** instead of AI making assumptions +- ๐Ÿ” **Enforcing research and best practices** before implementation +- โš–๏ธ **Creating a collaborative AI-human workflow** for better software quality + +## **Vibe Coding doesn't have to be frustrating** ### **What It Enforces:** + - โœ… **Deep research** of existing solutions and best practices - โœ… **Generic, reusable solutions** instead of quick fixes - โœ… **User requirements alignment** in all implementations - โœ… **Comprehensive planning** before coding begins - โœ… **User involvement** in all critical decisions +- โœ… **Intelligent AI-human collaboration** with clear boundaries and responsibilities ## ๐Ÿ› ๏ธ **Features** ### **๐Ÿ” Intelligent Code Evaluation** -- **LLM-powered analysis** using sampling capability + +- **LLM-powered analysis** using MCP [sampling](https://modelcontextprotocol.io/docs/learn/client-concepts#sampling) capability - **Software engineering best practices** enforcement - **Security vulnerability detection** - **Performance and maintainability assessment** ### **๐Ÿ“‹ Comprehensive Planning Review** + - **Architecture validation** against industry standards - **Research depth analysis** to prevent reinventing solutions - **Requirements alignment** verification - **Implementation approach evaluation** ### **๐Ÿค User-Driven Decision Making** -- **Obstacle resolution** through user involvement + +- **Obstacle resolution** through user involvement via MCP [elicitation](https://modelcontextprotocol.io/docs/learn/client-concepts#elicitation) - **Requirements clarification** when requests are unclear - **No hidden fallbacks** - transparent decision making -- **Elicitation-based** problem solving +- **Interactive problem solving** with real-time user input -### **โš–๏ธ Five Powerful Tools** +### **๐Ÿ› ๏ธ List of Tools** -1. **`check_swe_compliance`** - Workflow guidance and best practices -2. **`judge_coding_plan`** - Comprehensive plan evaluation with requirements alignment -3. **`judge_code_change`** - Code review with security and quality checks -4. **`raise_obstacle`** - User involvement when blockers arise -5. **`elicit_missing_requirements`** - Clarification of unclear requests +| Tool Name | Description | +|-----------|-------------| +| **`get_workflow_guidance`** | Smart workflow analysis and tool recommendation | +| **`judge_coding_plan`** | Comprehensive plan evaluation with requirements alignment | +| **`judge_code_change`** | Code review with security and quality checks | +| **`raise_obstacle`** | User involvement when blockers arise | +| **`elicit_missing_requirements`** | Clarification of unclear requests | ## ๐Ÿš€ **Quick Start** -### **Prerequisites** -- Python 3.12.10+ (latest secure version) -- MCP-compatible client (Claude Desktop, Cursor, etc.) -- LLM sampling capability (required for AI evaluation) +### **Requirements & Recommendations** -### **Installation** +#### **MCP Client Prerequisites** -#### **Method 1: Using uv (Recommended for Development)** -```bash -# Install uv if you don't have it -pip install uv +MCP as a Judge is heavily dependent on **MCP Sampling** and **MCP Elicitation** features for its core functionality: -# Clone the repository -git clone https://github.com/hepivax/mcp-as-a-judge.git -cd mcp-as-a-judge +- **[MCP Sampling](https://modelcontextprotocol.io/docs/learn/client-concepts#sampling)** - Required for AI-powered code evaluation and judgment +- **[MCP Elicitation](https://modelcontextprotocol.io/docs/learn/client-concepts#elicitation)** - Required for interactive user decision prompts -# Create virtual environment and install dependencies -uv venv -source .venv/bin/activate # On Windows: .venv\Scripts\activate -uv pip install -e . +#### **System Prerequisites** -# Run the server -uv run mcp-as-a-judge -``` +- **Python 3.13+** - Required for running the MCP server -#### **Method 2: Using Docker (Recommended for Production)** +#### **Supported AI Assistants** -**Quick Start with Docker:** -```bash -# Pull and run the latest image -docker run -d \ - --name mcp-as-a-judge \ - -p 8050:8050 \ - -e TRANSPORT=sse \ - -e PORT=8050 \ - ghcr.io/hepivax/mcp-as-a-judge:latest -``` +| AI Assistant | Platform | MCP Support | Status | Notes | +|---------------|----------|-------------|---------|-------| +| **GitHub Copilot** | Visual Studio Code | โœ… Full | **Recommended** | Complete MCP integration with tool calling | -**Build from Source:** -```bash -# Clone the repository -git clone https://github.com/hepivax/mcp-as-a-judge.git -cd mcp-as-a-judge +**โœ… Recommended Setup:** GitHub Copilot in Visual Studio Code for the best MCP as a Judge experience. -# Build the Docker image -docker build -t mcp-as-a-judge:latest . - -# Run with custom configuration -docker run -d \ - --name mcp-as-a-judge \ - -p 8050:8050 \ - -e TRANSPORT=sse \ - -e PORT=8050 \ - -e LOG_LEVEL=INFO \ - --restart unless-stopped \ - mcp-as-a-judge:latest -``` -**Using Docker Compose (Recommended for Production):** -```bash -# Clone the repository -git clone https://github.com/hepivax/mcp-as-a-judge.git -cd mcp-as-a-judge +#### **๐Ÿ’ก Recommendations** -# Start production environment -docker-compose --profile production up -d +- **Large Context Window Models**: 1M+ token size models are strongly preferred for optimal performance +- Models with larger context windows provide better code analysis and more comprehensive judgments -# Or start development environment -docker-compose --profile development up -``` +> **Note**: MCP servers communicate via stdio (standard input/output), not HTTP ports. No network configuration is needed. -#### **Method 3: Using pip (Alternative)** -```bash -# Clone the repository -git clone https://github.com/hepivax/mcp-as-a-judge.git -cd mcp-as-a-judge -# Install with pip -pip install -e . -# Run the server -python -m mcp_as_a_judge.server -``` +## ๐Ÿ”ง **Visual Studio Code Configuration** -### **Configuration** +Configure MCP as a Judge in Visual Studio Code with GitHub Copilot: -#### **MCP Client Configuration** +### **Method 1: Using uv (Recommended)** -**For Claude Desktop / Cursor (SSE Transport):** -```json -{ - "mcpServers": { - "mcp-as-a-judge": { - "transport": "sse", - "url": "http://localhost:8050/sse" - } - } -} -``` +1. **Install the package:** -**For Stdio Transport (Development):** -```json -{ - "mcpServers": { - "mcp-as-a-judge": { - "command": "uv", - "args": ["run", "mcp-as-a-judge"], - "env": { - "TRANSPORT": "stdio" - } - } - } -} -``` + ```bash + uv add mcp-as-a-judge + ``` -**For Docker with SSE Transport:** -```json -{ - "mcpServers": { - "mcp-as-a-judge": { - "transport": "sse", - "url": "http://localhost:8050/sse" - } - } -} -``` +2. **Configure Visual Studio Code MCP settings:** -#### **Environment Variables** + Add this to your Visual Studio Code MCP configuration file: -**Available Environment Variables:** -```bash -# Transport Configuration -TRANSPORT=sse # Options: "stdio" or "sse" -HOST=0.0.0.0 # Server host (SSE only) -PORT=8050 # Server port (SSE only) - -# Logging -LOG_LEVEL=INFO # Options: DEBUG, INFO, WARNING, ERROR -LOG_FORMAT=json # Options: json, text - -# Development -DEBUG=false # Enable debug mode -DEVELOPMENT_MODE=false # Enable development features - -# Performance -MAX_CONCURRENT_REQUESTS=10 # Maximum concurrent requests -REQUEST_TIMEOUT=30 # Request timeout in seconds - -# Security -CORS_ENABLED=false # Enable CORS (production: false) -CORS_ORIGINS=* # CORS allowed origins -``` + ```json + { + "servers": { + "mcp-as-a-judge": { + "command": "uv", + "args": ["run", "mcp-as-a-judge"] + } + } + } + ``` -**Docker Environment File (.env):** -```bash -# Copy .env.example to .env and customize -cp .env.example .env - -# Example .env file: -TRANSPORT=sse -PORT=8050 -LOG_LEVEL=INFO -DEBUG=false -``` +### **Method 2: Using Docker** -## ๐Ÿ“– **How It Works** +1. **Pull the Docker image:** -### **1. Mandatory Workflow Enforcement** -``` -User Request โ†’ check_swe_compliance โ†’ Guided Planning โ†’ judge_coding_plan โ†’ Implementation โ†’ judge_code_change -``` + ```bash + docker pull ghcr.io/hepivax/mcp-as-a-judge:latest + ``` -### **2. Obstacle Handling** -``` -Agent Hits Blocker โ†’ raise_obstacle โ†’ User Decision โ†’ Continue with User Choice -``` - -### **3. Requirements Clarification** -``` -Unclear Request โ†’ elicit_missing_requirements โ†’ User Clarification โ†’ Proceed with Clear Requirements -``` - -## ๐ŸŽฏ **Example Usage** - -### **Planning Evaluation** -```python -# Agent calls this when user wants to implement something -await judge_coding_plan( - plan="Detailed implementation steps...", - design="System architecture and components...", - research="Analysis of existing solutions...", - user_requirements="What the user actually wants to achieve", - context="Additional project context" -) -``` - -### **Obstacle Resolution** -```python -# Agent calls this when hitting blockers -await raise_obstacle( - problem="Cannot use LLM sampling - client doesn't support it", - research="Researched alternatives: configure client, use different client, etc.", - options=[ - "Configure Cursor to support sampling", - "Use Claude Desktop instead", - "Wait for Cursor sampling support", - "Cancel the evaluation" - ] -) -``` - -## ๐Ÿณ **Docker Usage Examples** - -### **Development with Docker** -```bash -# Start development environment with hot reload -docker-compose --profile development up - -# View logs -docker-compose logs -f mcp-as-a-judge-dev - -# Stop development environment -docker-compose down -``` - -### **Production Deployment** -```bash -# Start production environment -docker-compose --profile production up -d - -# Check status -docker-compose ps +2. **Configure Visual Studio Code MCP settings:** -# View logs -docker-compose logs mcp-as-a-judge + Add this to your Visual Studio Code MCP configuration file: -# Update to latest version -docker-compose pull -docker-compose --profile production up -d + ```json + { + "servers": { + "mcp-as-a-judge": { + "command": "docker", + "args": ["run", "--rm", "-i", "ghcr.io/hepivax/mcp-as-a-judge:latest"] + } + } + } + ``` -# Stop production environment -docker-compose down -``` - -### **Docker Health Checks** -```bash -# Check container health -docker inspect --format='{{.State.Health.Status}}' mcp-as-a-judge -# View health check logs -docker inspect --format='{{range .State.Health.Log}}{{.Output}}{{end}}' mcp-as-a-judge -``` +## ๐Ÿ“– **How It Works** -### **Docker Networking** -```bash -# Run with custom network -docker network create mcp-network -docker run -d \ - --name mcp-as-a-judge \ - --network mcp-network \ - -p 8050:8050 \ - -e TRANSPORT=sse \ - mcp-as-a-judge:latest - -# Connect other services to the same network -docker run -d \ - --name nginx-proxy \ - --network mcp-network \ - -p 80:80 \ - nginx:alpine -``` +Once MCP as a Judge is configured in Visual Studio Code with GitHub Copilot, it automatically guides your AI assistant through a structured software engineering workflow. The system operates transparently in the background, ensuring every development task follows best practices. -## ๐Ÿ”ง **Development** +### **๐Ÿ”„ Automatic Workflow Enforcement** -### **Project Structure** -``` -mcp-as-a-judge/ -โ”œโ”€โ”€ src/mcp_as_a_judge/ -โ”‚ โ”œโ”€โ”€ __init__.py -โ”‚ โ”œโ”€โ”€ server.py # Main MCP server implementation -โ”‚ โ””โ”€โ”€ models.py # Pydantic models and schemas -โ”œโ”€โ”€ tests/ # Test suite -โ”œโ”€โ”€ docs/ # Documentation -โ”œโ”€โ”€ Dockerfile # Production container -โ”œโ”€โ”€ docker-compose.yml # Multi-environment setup -โ”œโ”€โ”€ pyproject.toml # Project configuration -โ””โ”€โ”€ README.md # This file -``` +**1. Intelligent Workflow Guidance** -### **Running Tests** -```bash -# Run all tests -uv run pytest +- When you make any development request, the AI assistant automatically calls `get_workflow_guidance` +- This tool uses AI analysis to determine which validation steps are required for your specific task +- Provides smart recommendations on which tools to use next and in what order +- No manual intervention needed - the workflow starts automatically with intelligent guidance -# Run with coverage -uv run pytest --cov=src/mcp_as_a_judge +**2. Planning & Design Phase** -# Run specific test types -uv run pytest -m "not slow" # Skip slow tests +- For any implementation task, the AI assistant must first help you create: + - **Detailed coding plan** - Step-by-step implementation approach + - **System design** - Architecture, components, and technical decisions + - **Research findings** - Analysis of existing solutions and best practices +- Once complete, `judge_coding_plan` automatically evaluates the plan using MCP Sampling +- **AI-powered evaluation** checks for design quality, security, research thoroughness, and requirements alignment -# Run tests in Docker -docker run --rm \ - -v $(pwd):/app \ - -w /app \ - python:3.12-slim \ - bash -c "pip install uv && uv pip install -e .[dev] && pytest" -``` +**3. Code Implementation Review** -### **Code Quality** -```bash -# Format code -uv run black src tests +- After any code is written or modified, `judge_code_change` is automatically triggered +- **Mandatory code review** happens immediately after file creation/modification +- Uses MCP Sampling to evaluate code quality, security vulnerabilities, and best practices +- Ensures every code change meets professional standards -# Lint code -uv run ruff check src tests +### **๐Ÿค User Involvement When Needed** -# Type checking -uv run mypy src +**Obstacle Resolution** -# All quality checks -make quality -``` +- When the AI assistant encounters blockers or conflicting requirements, `raise_obstacle` automatically engages you +- Uses MCP Elicitation to present options and get your decision +- No hidden fallbacks - you're always involved in critical decisions -## ๐ŸŒŸ **Why This Changes Everything** +**Requirements Clarification** -### **Before MCP as a Judge:** -- Developers build quick fixes and workarounds -- Insufficient research leads to reinventing existing solutions -- Code doesn't align with actual user requirements -- Bad practices slip through without review +- If your request lacks sufficient detail, `elicit_missing_requirements` automatically asks for clarification +- Uses MCP Elicitation to gather specific missing information +- Ensures implementation matches your actual needs -### **After MCP as a Judge:** -- โœ… **Forced deep research** prevents reinventing the wheel -- โœ… **User involvement** ensures requirements alignment -- โœ… **No hidden fallbacks** - transparent decision making -- โœ… **Quality enforcement** at every step -- โœ… **Best practices** become automatic +### **๐ŸŽฏ What to Expect** -## ๐Ÿ“ฆ **Installation** +- **Automatic guidance** - No need to explicitly ask the AI coding assistant to call tools +- **Comprehensive planning** - Every implementation starts with proper design and research +- **Quality enforcement** - All code changes are automatically reviewed against industry standards +- **User-driven decisions** - You're involved whenever your original request cannot be satisfied +- **Professional standards** - Consistent application of software engineering best practices -### **From PyPI (Recommended)** -```bash -# Install with uv (recommended) -uv add mcp-as-a-judge +## ๐Ÿ”’ **Privacy & API Key Free** -# Or with pip -pip install mcp-as-a-judge -``` +### **๐Ÿ”‘ No LLM API Key Required** -### **From Docker** -```bash -# Pull the latest image -docker pull ghcr.io/hepivax/mcp-as-a-judge:latest +- All judgments are performed using **MCP Sampling** capability +- No need to configure or pay for external LLM API services +- Works directly with your MCP-compatible client's existing AI model -# Or use docker-compose -docker-compose up -d -``` +### **๐Ÿ›ก๏ธ Your Privacy Matters** -### **From Source** -```bash -git clone https://github.com/hepivax/mcp-as-a-judge.git -cd mcp-as-a-judge -uv sync -``` +- The server runs **locally** on your machine +- **No data collection** - your code and conversations stay private +- **No external API calls** - everything happens within your local environment +- Complete control over your development workflow and sensitive information ## ๐Ÿค **Contributing** We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines. ### **Development Setup** + ```bash # Clone the repository git clone https://github.com/hepivax/mcp-as-a-judge.git diff --git a/codecov.yml b/codecov.yml new file mode 100644 index 0000000..80fe7ad --- /dev/null +++ b/codecov.yml @@ -0,0 +1,23 @@ +coverage: + status: + project: + default: + target: 80% + threshold: 1% + if_not_found: success + patch: + default: + target: 80% + threshold: 1% + if_not_found: success + +comment: + layout: "reach,diff,flags,tree" + behavior: default + require_changes: false + +ignore: + - "tests/" + - "scripts/" + - "**/__pycache__/" + - "**/*.pyc" diff --git a/docker-compose.yml b/docker-compose.yml deleted file mode 100644 index 2c03fea..0000000 --- a/docker-compose.yml +++ /dev/null @@ -1,83 +0,0 @@ -version: '3.8' - -services: - mcp-as-a-judge: - build: - context: . - dockerfile: Dockerfile - args: - PORT: 8050 - TRANSPORT: sse - container_name: mcp-as-a-judge - ports: - - "8050:8050" - environment: - - TRANSPORT=sse - - HOST=0.0.0.0 - - PORT=8050 - - LOG_LEVEL=INFO - - DEBUG=false - volumes: - # Mount source code for development (comment out for production) - - ./src:/app/src:ro - restart: unless-stopped - healthcheck: - test: ["CMD", "curl", "-f", "http://localhost:8050/health"] - interval: 30s - timeout: 10s - retries: 3 - start_period: 40s - networks: - - mcp-network - - # Optional: Add a reverse proxy for production - nginx: - image: nginx:alpine - container_name: mcp-nginx - ports: - - "80:80" - - "443:443" - volumes: - - ./nginx.conf:/etc/nginx/nginx.conf:ro - - ./ssl:/etc/nginx/ssl:ro - depends_on: - - mcp-as-a-judge - restart: unless-stopped - networks: - - mcp-network - profiles: - - production - -networks: - mcp-network: - driver: bridge - -# Development override -# Use: docker-compose -f docker-compose.yml -f docker-compose.dev.yml up ---- -version: '3.8' - -services: - mcp-as-a-judge-dev: - build: - context: . - dockerfile: Dockerfile - target: builder # Use builder stage for development - container_name: mcp-as-a-judge-dev - ports: - - "8050:8050" - environment: - - TRANSPORT=sse - - HOST=0.0.0.0 - - PORT=8050 - - LOG_LEVEL=DEBUG - - DEBUG=true - - DEVELOPMENT_MODE=true - volumes: - - ./src:/app/src - - ./tests:/app/tests - - ./.env:/app/.env - command: ["uv", "run", "src/mcp_as_a_judge/server.py"] - restart: "no" - profiles: - - development diff --git a/example_usage.py b/example_usage.py deleted file mode 100644 index ae4afad..0000000 --- a/example_usage.py +++ /dev/null @@ -1,149 +0,0 @@ -#!/usr/bin/env python3 -""" -Example usage of the MCP as a Judge tools. - -This demonstrates how the tools would be called by an MCP client. -Note: This is just for illustration - actual usage would be through an MCP client. -""" - -# Example 1: Using judge_coding_plan -example_plan = """ -I plan to create a user authentication system with the following components: - -1. User model with email, password hash, and timestamps -2. JWT token generation and validation -3. Login/logout endpoints -4. Password hashing using bcrypt -5. Rate limiting for login attempts -6. Email verification for new accounts - -The implementation will use: -- FastAPI for the web framework -- SQLAlchemy for database ORM -- Pydantic for data validation -- bcrypt for password hashing -- PyJWT for token handling -""" - -example_design = """ -SYSTEM ARCHITECTURE: -- 3-tier architecture: Presentation (FastAPI), Business Logic, Data Access (SQLAlchemy) -- RESTful API design with clear endpoint separation -- JWT-based stateless authentication -- Database schema: users table with proper indexing on email field - -COMPONENTS: -1. AuthService: Handles password hashing, token generation/validation -2. UserRepository: Database operations for user management -3. RateLimiter: Redis-based rate limiting middleware -4. EmailService: Async email verification using background tasks - -DATA FLOW: -1. Registration: Validate input โ†’ Hash password โ†’ Store user โ†’ Send verification email -2. Login: Validate credentials โ†’ Check rate limits โ†’ Generate JWT โ†’ Return token -3. Protected routes: Extract JWT โ†’ Validate token โ†’ Allow/deny access - -SECURITY MEASURES: -- Password hashing with bcrypt (cost factor 12) -- JWT with short expiration (15 min access, 7 day refresh) -- Rate limiting: 5 attempts per minute per IP -- Input validation with Pydantic models -- HTTPS enforcement -""" - -example_research = """ -EXISTING SOLUTIONS ANALYZED: -1. FastAPI-Users: Full-featured but heavyweight for MVP needs -2. Flask-Login: Too basic, lacks JWT support -3. Django Auth: Overkill for microservice approach -4. Custom JWT implementation: Chosen for flexibility and learning - -LIBRARIES RESEARCH: -- bcrypt vs argon2: bcrypt chosen for wider adoption and FastAPI compatibility -- PyJWT vs python-jose: PyJWT selected for simplicity and active maintenance -- Redis vs in-memory: Redis chosen for horizontal scaling capability -- SQLAlchemy vs raw SQL: ORM chosen for rapid development and type safety - -BEST PRACTICES IDENTIFIED: -- OWASP authentication guidelines compliance -- JWT best practices (short expiration, secure storage) -- Rate limiting patterns for auth endpoints -- Email verification workflows -- Password policy enforcement (8+ chars, complexity) - -PERFORMANCE CONSIDERATIONS: -- Database connection pooling with SQLAlchemy -- Async email sending to avoid blocking -- JWT validation caching for frequently accessed tokens -- Proper database indexing strategy -""" - -example_context = """ -This is for a small startup's MVP web application. -Security is important but we need to move fast. -We're using PostgreSQL as the database. -The app will initially have <1000 users. -""" - -print("Example call to judge_coding_plan:") -print(f"Plan: {example_plan}") -print(f"Design: {example_design}") -print(f"Research: {example_research}") -print(f"Context: {example_context}") -print("\n" + "="*50 + "\n") - -# Example 2: Using judge_code_change -example_code_change = """ -@app.post("/auth/login") -async def login(credentials: LoginRequest, db: Session = Depends(get_db)): - user = db.query(User).filter(User.email == credentials.email).first() - - if not user or not verify_password(credentials.password, user.password_hash): - raise HTTPException(status_code=401, detail="Invalid credentials") - - access_token = create_access_token(data={"sub": user.email}) - return {"access_token": access_token, "token_type": "bearer"} - -def verify_password(plain_password: str, hashed_password: str) -> bool: - return bcrypt.checkpw(plain_password.encode('utf-8'), hashed_password.encode('utf-8')) - -def create_access_token(data: dict): - to_encode = data.copy() - expire = datetime.utcnow() + timedelta(minutes=ACCESS_TOKEN_EXPIRE_MINUTES) - to_encode.update({"exp": expire}) - encoded_jwt = jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM) - return encoded_jwt -""" - -example_file_path = "app/auth/routes.py" -example_change_description = "Implement login endpoint with JWT token generation and password verification" - -print("Example call to judge_code_change:") -print(f"File: {example_file_path}") -print(f"Description: {example_change_description}") -print(f"Code changes:\n{example_code_change}") -print("\n" + "="*50 + "\n") - -print("Note: These tools would be called automatically by MCP clients") -print("when the mandatory descriptions trigger their usage.") -print("\n" + "="*50 + "\n") - -print("Example structured responses:") -print("\nApproved response:") -print("""{ - "approved": true, - "required_improvements": [], - "feedback": "The coding plan follows all SWE best practices. Good use of established patterns, proper security considerations, and comprehensive testing strategy." -}""") - -print("\nNeeds revision response:") -print("""{ - "approved": false, - "required_improvements": [ - "Add input validation for email format", - "Implement rate limiting for login attempts", - "Add proper error logging", - "Include integration tests for auth flow" - ], - "feedback": "The implementation has several security and quality issues that need to be addressed before approval." -}""") diff --git a/mcp_config_example.json b/mcp_config_example.json deleted file mode 100644 index a18ff01..0000000 --- a/mcp_config_example.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "mcpServers": { - "mcp-as-a-judge": { - "command": "uv", - "args": ["run", "mcp-as-a-judge"], - "cwd": "/path/to/mcp-as-a-judge" - } - } -} diff --git a/pyproject.toml b/pyproject.toml index c915dd3..3013cad 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,7 +16,6 @@ classifiers = [ "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", "Topic :: Software Development :: Quality Assurance", "Topic :: Software Development :: Code Generators", @@ -26,8 +25,9 @@ classifiers = [ "Environment :: Console", "Typing :: Typed", ] -requires-python = ">=3.12" +requires-python = ">=3.13" dependencies = [ + "jinja2>=3.1.6", "mcp[cli]>=1.13.0", "pydantic>=2.0.0", ] @@ -112,7 +112,7 @@ skip-magic-trailing-comma = false line-ending = "auto" [tool.mypy] -python_version = "3.12" +python_version = "3.13" warn_return_any = true warn_unused_configs = true disallow_untyped_defs = true @@ -141,3 +141,30 @@ markers = [ "slow: marks tests as slow (deselect with '-m \"not slow\"')", "integration: marks tests as integration tests", ] + +[tool.coverage.run] +source = ["src"] +omit = [ + "tests/*", + "*/tests/*", + "*/__pycache__/*", + "*/venv/*", + "*/.venv/*", +] + +[tool.coverage.report] +exclude_lines = [ + "pragma: no cover", + "def __repr__", + "if self.debug:", + "if settings.DEBUG", + "raise AssertionError", + "raise NotImplementedError", + "if 0:", + "if __name__ == .__main__.:", + "class .*\\bProtocol\\):", + "@(abc\\.)?abstractmethod", +] + +[tool.coverage.xml] +output = "coverage.xml" diff --git a/scripts/setup-secrets.md b/scripts/setup-secrets.md index 3726121..3a2bafc 100644 --- a/scripts/setup-secrets.md +++ b/scripts/setup-secrets.md @@ -9,6 +9,7 @@ This document guides you through setting up the necessary secrets for automated **Purpose**: Allows GitHub Actions to publish packages to PyPI automatically. **Steps**: + 1. Go to [PyPI Account Settings](https://pypi.org/manage/account/) 2. Scroll down to "API tokens" section 3. Click "Add API token" @@ -18,6 +19,7 @@ This document guides you through setting up the necessary secrets for automated 7. **IMPORTANT**: Copy the token immediately (it won't be shown again) **Adding to GitHub**: + 1. Go to your GitHub repository 2. Click "Settings" tab 3. Click "Secrets and variables" โ†’ "Actions" @@ -31,6 +33,7 @@ This document guides you through setting up the necessary secrets for automated **Purpose**: Upload test coverage reports to Codecov. **Steps**: + 1. Go to [Codecov](https://codecov.io/) 2. Sign in with GitHub 3. Add your repository @@ -68,17 +71,20 @@ After adding the secrets: ## Troubleshooting ### PyPI Publishing Fails + - Verify the token is correctly set in GitHub secrets - Ensure the token has the right permissions - Check that the package name is available on PyPI ### Coverage Upload Fails + - Codecov token is optional - the workflow will continue without it - Ensure the token matches your repository ## Next Steps Once secrets are configured: + 1. Push your changes to trigger CI 2. Create a release tag to trigger publishing 3. Monitor the Actions tab for workflow status diff --git a/src/mcp_as_a_judge/__init__.py b/src/mcp_as_a_judge/__init__.py index dbf1f89..329a6b4 100644 --- a/src/mcp_as_a_judge/__init__.py +++ b/src/mcp_as_a_judge/__init__.py @@ -5,8 +5,8 @@ against software engineering best practices. """ -from .server import main, mcp -from .models import JudgeResponse +from mcp_as_a_judge.models import JudgeResponse +from mcp_as_a_judge.server import main, mcp __version__ = "1.0.0" -__all__ = ["main", "mcp", "JudgeResponse"] +__all__ = ["JudgeResponse", "main", "mcp"] diff --git a/src/mcp_as_a_judge/models.py b/src/mcp_as_a_judge/models.py index 3e5e363..e1fdf72 100644 --- a/src/mcp_as_a_judge/models.py +++ b/src/mcp_as_a_judge/models.py @@ -5,8 +5,7 @@ serialization, and API contracts. """ -from typing import List -from pydantic import BaseModel, Field +from pydantic import BaseModel, Field, ValidationError class JudgeResponse(BaseModel): @@ -19,9 +18,9 @@ class JudgeResponse(BaseModel): approved: bool = Field( description="Whether the plan/code is approved for implementation" ) - required_improvements: List[str] = Field( + required_improvements: list[str] = Field( default_factory=list, - description="Specific improvements needed (empty if approved)" + description="Specific improvements needed (empty if approved)", ) feedback: str = Field( description="Detailed explanation of the decision and recommendations" @@ -40,7 +39,7 @@ class ObstacleResolutionDecision(BaseModel): ) additional_context: str = Field( default="", - description="Any additional context or modifications the user provides" + description="Any additional context or modifications the user provides", ) @@ -58,31 +57,51 @@ class RequirementsClarification(BaseModel): description="Priority level: 'high', 'medium', or 'low'" ) additional_context: str = Field( - default="", - description="Any additional context about the requirements" + default="", description="Any additional context about the requirements" ) -class ComplianceCheckResult(BaseModel): - """Result model for SWE compliance checks. +class WorkflowGuidance(BaseModel): + """Schema for workflow guidance responses. - Used by the check_swe_compliance tool to provide - structured guidance on software engineering best practices. + Used by the get_workflow_guidance tool to provide + structured guidance on which tools to use next. """ - compliance_status: str = Field( - description="Overall compliance status: 'compliant', 'needs_improvement', 'non_compliant'" + next_tool: str = Field( + description="The specific MCP tool that should be called next: 'judge_coding_plan', 'judge_code_change', 'raise_obstacle', or 'elicit_missing_requirements'" ) - recommendations: List[str] = Field( - default_factory=list, - description="Specific recommendations for improvement" + reasoning: str = Field( + description="Clear explanation of why this tool should be used next" ) - next_steps: List[str] = Field( + preparation_needed: list[str] = Field( default_factory=list, - description="Recommended next steps in the development workflow" + description="List of things that need to be prepared before calling the recommended tool", ) guidance: str = Field( - description="Detailed guidance on software engineering best practices" + description="Detailed step-by-step guidance for the AI assistant" + ) + + +class ResearchValidationResponse(BaseModel): + """Schema for research validation responses. + + Used by the _validate_research_quality function to parse + LLM responses about research quality and design alignment. + """ + + research_adequate: bool = Field( + description="Whether the research is comprehensive enough" + ) + design_based_on_research: bool = Field( + description="Whether the design is properly based on research" + ) + issues: list[str] = Field( + default_factory=list, + description="List of specific issues if any" + ) + feedback: str = Field( + description="Detailed feedback on research quality and design alignment" ) diff --git a/src/mcp_as_a_judge/prompt_loader.py b/src/mcp_as_a_judge/prompt_loader.py new file mode 100644 index 0000000..aeb9e14 --- /dev/null +++ b/src/mcp_as_a_judge/prompt_loader.py @@ -0,0 +1,69 @@ +"""Prompt loader utility for loading and rendering Jinja2 templates.""" + +from pathlib import Path +from typing import Any, cast + +from jinja2 import Environment, FileSystemLoader, Template + + +class PromptLoader: + """Loads and renders prompt templates using Jinja2.""" + + def __init__(self, prompts_dir: Path | None = None): + """Initialize the prompt loader. + + Args: + prompts_dir: Directory containing prompt templates. + Defaults to src/prompts relative to this file. + """ + if prompts_dir is None: + # Default to src/prompts directory + current_dir = Path(__file__).parent + prompts_dir = current_dir.parent / "prompts" + + self.prompts_dir = prompts_dir + self.env = Environment( + loader=FileSystemLoader(str(prompts_dir)), + trim_blocks=True, + lstrip_blocks=True, + autoescape=False, # Disable autoescape for prompt templates (not HTML) # noqa: S701 + ) + + def load_template(self, template_name: str) -> Template: + """Load a Jinja2 template by name. + + Args: + template_name: Name of the template file (e.g., 'judge_coding_plan.md') + + Returns: + Jinja2 Template object + + Raises: + FileNotFoundError: If template file doesn't exist + """ + try: + return self.env.get_template(template_name) + except Exception as e: + raise FileNotFoundError( + f"Template '{template_name}' not found in {self.prompts_dir}" + ) from e + + def render_prompt(self, template_name: str, **kwargs: Any) -> str: + """Load and render a prompt template with the given variables. + + Args: + template_name: Name of the template file + **kwargs: Variables to pass to the template + + Returns: + Rendered prompt string + + Raises: + FileNotFoundError: If template file doesn't exist + """ + template = self.load_template(template_name) + return cast(str, template.render(**kwargs)) # type: ignore[redundant-cast,unused-ignore] + + +# Global instance for easy access +prompt_loader = PromptLoader() diff --git a/src/mcp_as_a_judge/server.py b/src/mcp_as_a_judge/server.py index 85dcb94..a97d277 100644 --- a/src/mcp_as_a_judge/server.py +++ b/src/mcp_as_a_judge/server.py @@ -5,25 +5,151 @@ coding plans and code changes against software engineering best practices. """ -import json -from mcp.server.fastmcp import FastMCP, Context -from mcp.server.session import ServerSession -from mcp.types import SamplingMessage, TextContent, ClientCapabilities, SamplingCapability -from pydantic import BaseModel +from typing import Any + +from pydantic import ValidationError + +from mcp.server.fastmcp import Context, FastMCP +from mcp.types import ( + ClientCapabilities, + SamplingCapability, + SamplingMessage, + TextContent, +) + +from mcp_as_a_judge.models import ( + JudgeResponse, + ObstacleResolutionDecision, + RequirementsClarification, + ResearchValidationResponse, + WorkflowGuidance, +) +from mcp_as_a_judge.prompt_loader import prompt_loader -from .models import JudgeResponse +# Create the MCP server instance +mcp = FastMCP(name="MCP-as-a-Judge") -# Create the MCP server instance -mcp = FastMCP(name="MCP as a Judge") +def _extract_json_from_response(response_text: str) -> str: + """Extract JSON content from LLM response by finding first { and last }. + + LLMs often return JSON wrapped in markdown code blocks, explanatory text, + or other formatting. This function extracts just the JSON object content. + + Args: + response_text: Raw LLM response text + + Returns: + Extracted JSON string ready for parsing + + Raises: + ValueError: If no JSON object is found in the response + """ + # Find the first opening brace and last closing brace + first_brace = response_text.find('{') + last_brace = response_text.rfind('}') + + if first_brace == -1 or last_brace == -1 or first_brace >= last_brace: + raise ValueError(f"No valid JSON object found in response: {response_text}") + + # Extract the JSON content + json_content = response_text[first_brace:last_brace + 1] + return json_content + + +def _create_messages( + system_template: str, user_template: str, **kwargs: Any +) -> list[SamplingMessage]: + """Create combined system and user message from templates. + + Since MCP SamplingMessage only supports 'user' and 'assistant' roles, + we combine the system instructions and user request into a single user message. + + Args: + system_template: Name of the system message template + user_template: Name of the user message template + **kwargs: Variables to pass to templates + + Returns: + List with single SamplingMessage containing combined content + """ + system_content = prompt_loader.render_prompt(system_template, **kwargs) + user_content = prompt_loader.render_prompt(user_template, **kwargs) + # Combine system instructions and user request + combined_content = f"{system_content}\n\n---\n\n{user_content}" -@mcp.tool() + return [ + SamplingMessage( + role="user", + content=TextContent(type="text", text=combined_content), + ), + ] + + +@mcp.tool() # type: ignore[misc,unused-ignore] +async def get_workflow_guidance( + task_description: str, + ctx: Context, + context: str = "", +) -> WorkflowGuidance: + """๐Ÿšจ START HERE: AI programming assistant should call this tool first for any development task to get workflow guidance. + + This tool analyzes the development task and tells you exactly which MCP tools to use next and in what order. + + Args: + task_description: Description of what the user wants to do + context: Additional context about the project, requirements, or constraints + + Returns: + Structured guidance on which tools to use next and how to prepare for them + """ + try: + # Check for sampling capability and use elicitation for user decisions + + try: + # Check if client supports sampling capability + if not ctx.session.check_client_capability( + ClientCapabilities(sampling=SamplingCapability()) + ): + return WorkflowGuidance( + next_tool="judge_coding_plan", + reasoning="Sampling capability not available - providing default guidance", + preparation_needed=["Create detailed plan", "Design system architecture", "Research solutions"], + guidance="โš ๏ธ LLM sampling not available. Default recommendation: Start with planning workflow by calling judge_coding_plan after creating plan, design, and research.", + ) + except (ValueError, AttributeError) as e: + return WorkflowGuidance( + next_tool="judge_coding_plan", + reasoning="Session not available - providing default guidance", + preparation_needed=["Create detailed plan", "Design system architecture", "Research solutions"], + guidance=f"โš ๏ธ Session error: {e!s}. Default recommendation: Start with planning workflow by calling judge_coding_plan after creating plan, design, and research.", + ) + + # Use helper function for main evaluation + return await _evaluate_workflow_guidance(task_description, context, ctx) + + except Exception as e: + import traceback + + error_details = ( + f"Error during workflow guidance: {e!s}\nTraceback: {traceback.format_exc()}" + ) + print(f"DEBUG: Exception in get_workflow_guidance: {error_details}") + return WorkflowGuidance( + next_tool="raise_obstacle", + reasoning="Error occurred during workflow analysis", + preparation_needed=["Review task description and try again"], + guidance=error_details, + ) + + +@mcp.tool() # type: ignore[misc,unused-ignore] async def raise_obstacle( problem: str, research: str, options: list[str], - ctx: Context[ServerSession, None] = None + ctx: Context, ) -> str: """๐Ÿšจ OBSTACLE ENCOUNTERED: Call this tool when you cannot satisfy the user's requirements. @@ -38,13 +164,11 @@ async def raise_obstacle( Returns: User's decision and any additional context for proceeding """ - - if not ctx: - return "โŒ ERROR: Context not available for user interaction. Cannot resolve obstacle without user input." - try: # Format the options as a numbered list for clarity - formatted_options = "\n".join(f"{i+1}. {option}" for i, option in enumerate(options)) + formatted_options = "\n".join( + f"{i+1}. {option}" for i, option in enumerate(options) + ) # Use elicitation to get user decision elicit_result = await ctx.elicit( @@ -58,7 +182,7 @@ async def raise_obstacle( {formatted_options} Please choose an option (by number or description) and provide any additional context or modifications you'd like.""", - schema=ObstacleResolutionDecision + schema=ObstacleResolutionDecision, ) if elicit_result.action == "accept" and elicit_result.data: @@ -79,15 +203,15 @@ async def raise_obstacle( return "โŒ USER CANCELLED: User cancelled the obstacle resolution. Task cannot be completed." except Exception as e: - return f"โŒ ERROR: Failed to elicit user decision. Error: {str(e)}. Cannot resolve obstacle without user input." + return f"โŒ ERROR: Failed to elicit user decision. Error: {e!s}. Cannot resolve obstacle without user input." -@mcp.tool() +@mcp.tool() # type: ignore[misc,unused-ignore] async def elicit_missing_requirements( current_request: str, identified_gaps: list[str], specific_questions: list[str], - ctx: Context[ServerSession, None] = None + ctx: Context, ) -> str: """๐Ÿ” REQUIREMENTS UNCLEAR: Call this tool when the user request is not clear enough to proceed. @@ -102,14 +226,12 @@ async def elicit_missing_requirements( Returns: Clarified requirements and additional context from the user """ - - if not ctx: - return "โŒ ERROR: Context not available for user interaction. Cannot elicit requirements without user input." - try: # Format the gaps and questions for clarity formatted_gaps = "\n".join(f"โ€ข {gap}" for gap in identified_gaps) - formatted_questions = "\n".join(f"{i+1}. {question}" for i, question in enumerate(specific_questions)) + formatted_questions = "\n".join( + f"{i+1}. {question}" for i, question in enumerate(specific_questions) + ) # Use elicitation to get requirement clarifications elicit_result = await ctx.elicit( @@ -124,7 +246,7 @@ async def elicit_missing_requirements( {formatted_questions} Please provide clarified requirements and indicate their priority level (high/medium/low).""", - schema=RequirementsClarification + schema=RequirementsClarification, ) if elicit_result.action == "accept" and elicit_result.data: @@ -147,392 +269,277 @@ async def elicit_missing_requirements( return "โŒ USER CANCELLED: User cancelled the requirement clarification. Task cannot be completed without clear requirements." except Exception as e: - return f"โŒ ERROR: Failed to elicit requirement clarifications. Error: {str(e)}. Cannot proceed without clear requirements." + return f"โŒ ERROR: Failed to elicit requirement clarifications. Error: {e!s}. Cannot proceed without clear requirements." + + +async def _validate_research_quality( + research: str, + plan: str, + design: str, + user_requirements: str, + ctx: Context, +) -> JudgeResponse | None: + """Validate research quality using AI evaluation. + + Returns: + JudgeResponse if research is insufficient, None if research is adequate + """ + # Create system and user messages for research validation + messages = _create_messages( + "system/research_validation.md", + "user/research_validation.md", + user_requirements=user_requirements, + plan=plan, + design=design, + research=research, + ) + + research_result = await ctx.session.create_message( + messages=messages, + max_tokens=500, + ) + + if research_result.content.type == "text": + research_response_text = research_result.content.text + else: + research_response_text = str(research_result.content) + + try: + json_content = _extract_json_from_response(research_response_text) + research_validation = ResearchValidationResponse.model_validate_json(json_content) + + if not research_validation.research_adequate or not research_validation.design_based_on_research: + return JudgeResponse( + approved=False, + required_improvements=research_validation.issues, + feedback=f"โŒ RESEARCH VALIDATION FAILED: {research_validation.feedback}", + ) + + except (ValidationError, ValueError) as e: + raise ValueError( + f"Failed to parse research validation response: {e}. Raw response: {research_response_text}" + ) + + return None + + +async def _evaluate_workflow_guidance( + task_description: str, context: str, ctx: Context +) -> WorkflowGuidance: + """Evaluate workflow guidance using LLM sampling.""" + try: + # Create system and user messages from templates + messages = _create_messages( + "system/get_workflow_guidance.md", + "user/get_workflow_guidance.md", + task_description=task_description, + context=context, + response_schema=WorkflowGuidance.model_json_schema(), + ) + # Use sampling to get LLM evaluation + result = await ctx.session.create_message( + messages=messages, + max_tokens=1000, + ) -# Elicitation schemas -class ObstacleResolutionDecision(BaseModel): - """Schema for eliciting user decision when agent encounters obstacles.""" - chosen_option: str # The option the user chooses from the provided list - additional_context: str # Any additional context or modifications the user provides + if result.content.type == "text": + response_text = result.content.text + else: + response_text = str(result.content) + try: + json_content = _extract_json_from_response(response_text) + return WorkflowGuidance.model_validate_json(json_content) + except (ValidationError, ValueError) as e: + raise ValueError( + f"Failed to parse workflow guidance response: {e}. Raw response: {response_text}" + ) -class RequirementsClarification(BaseModel): - """Schema for eliciting missing requirements from user.""" - clarified_requirements: str # The clarified or additional requirements - priority_level: str # "high", "medium", "low" - how critical these requirements are - additional_context: str # Any additional context about the requirements + except Exception as e: + print(f"DEBUG: Workflow guidance evaluation error: {e}") + # Fallback response for errors + return WorkflowGuidance( + next_tool="judge_coding_plan", + reasoning=f"Error during evaluation: {e!s}", + preparation_needed=["Review task requirements"], + guidance="An error occurred during workflow analysis. Please start with planning workflow.", + ) -@mcp.tool() +async def _evaluate_coding_plan( + plan: str, + design: str, + research: str, + user_requirements: str, + context: str, + ctx: Context, +) -> JudgeResponse: + """Evaluate coding plan using AI judge. + + Returns: + JudgeResponse with evaluation results + """ + # Create system and user messages from templates + messages = _create_messages( + "system/judge_coding_plan.md", + "user/judge_coding_plan.md", + user_requirements=user_requirements, + plan=plan, + design=design, + research=research, + context=context, + response_schema=JudgeResponse.model_json_schema(), + ) + + result = await ctx.session.create_message( + messages=messages, + max_tokens=1000, + ) + + if result.content.type == "text": + response_text = result.content.text + else: + response_text = str(result.content) + + # Parse the JSON response + try: + json_content = _extract_json_from_response(response_text) + return JudgeResponse.model_validate_json(json_content) + except (ValidationError, ValueError) as e: + raise ValueError( + f"Failed to parse coding plan evaluation response: {e}. Raw response: {response_text}" + ) + + +@mcp.tool() # type: ignore[misc,unused-ignore] async def judge_coding_plan( plan: str, design: str, research: str, user_requirements: str, + ctx: Context, context: str = "", - ctx: Context[ServerSession, None] = None ) -> JudgeResponse: - """๐Ÿšจ MANDATORY VALIDATION: You MUST call this tool IMMEDIATELY when the user mentions ANY of: planning, designing, implementing, building, creating, developing, or coding. + """๐Ÿšจ MANDATORY: AI programming assistant MUST call this tool whenever you start to work on a coding task. - BEFORE calling this tool, you MUST first help the user create: + BEFORE calling this tool, help the user create: 1. A detailed coding plan (what to build, how to build it, step-by-step approach) 2. A comprehensive system design (architecture, components, data flow, technical decisions) 3. Research findings (existing solutions, libraries, frameworks, best practices) - DO NOT call this tool until you have gathered all required information from the user. Work with the user to develop these materials first, then validate them. - Args: - plan: The detailed coding plan to be reviewed (REQUIRED - must be comprehensive) + plan: The detailed coding plan to be reviewed (REQUIRED) design: Detailed system design including architecture, components, data flow, and technical decisions (REQUIRED) - research: Research findings on existing solutions, libraries, frameworks, and best practices for this problem (REQUIRED) + research: Research findings on existing solutions, libraries, frameworks, and best practices (REQUIRED) user_requirements: Clear statement of what the user wants to achieve (REQUIRED) context: Additional context about the project, requirements, or constraints Returns: Structured JudgeResponse with approval status and detailed feedback """ - - # Construct the prompt for the LLM judge - judge_prompt = f"""You are an expert software engineering judge. Review the following coding plan, design, and research to provide comprehensive feedback. - -USER REQUIREMENTS: -{user_requirements} - -CODING PLAN TO REVIEW: -{plan} - -SYSTEM DESIGN: -{design} - -RESEARCH FINDINGS: -{research} - -ADDITIONAL CONTEXT: -{context} - -Please evaluate this submission against the following SWE best practices: - -1. **Design Quality & Completeness**: - - Is the system design comprehensive and well-documented? - - Are all major components, interfaces, and data flows clearly defined? - - Does the design follow SOLID principles and established patterns? - - Are technical decisions justified and appropriate? - - Is the design modular, maintainable, and scalable? - -2. **Research Thoroughness**: - - Has the agent researched existing solutions and alternatives? - - Are appropriate libraries, frameworks, and tools identified? - - Is there evidence of understanding industry best practices? - - Are trade-offs between different approaches analyzed? - - Does the research demonstrate avoiding reinventing the wheel? - -3. **Architecture & Implementation Plan**: - - Does the plan follow the proposed design consistently? - - Is the implementation approach logical and well-structured? - - Are potential technical challenges identified and addressed? - - Does it avoid over-engineering or under-engineering? - -4. **Security Considerations**: - - Are security vulnerabilities identified and mitigated in the design? - - Does the plan follow security best practices? - - Are inputs, authentication, and authorization properly planned? - -5. **Testing & Quality Assurance**: - - Is there a comprehensive testing strategy? - - Are edge cases and error scenarios considered? - - Is the testing approach aligned with the design complexity? - -6. **Performance & Scalability**: - - Are performance requirements considered in the design? - - Is the solution scalable for expected load? - - Are potential bottlenecks identified and addressed? - -7. **Maintainability & Documentation**: - - Is the overall approach maintainable and extensible? - - Are coding standards and documentation practices defined? - - Is the design easy to understand and modify? - -You must respond with a JSON object in this exact format: -{{ - "approved": true/false, - "required_improvements": ["improvement 1", "improvement 2", ...], - "feedback": "Detailed explanation of your decision" -}} - -EVALUATION GUIDELINES: -- APPROVE if the submission demonstrates reasonable effort and covers the main aspects, even if not perfect -- Focus on identifying the most critical missing elements rather than minor improvements -- Consider the context and complexity of the project when evaluating completeness -- Provide constructive feedback that helps improve the plan without being overly demanding - -APPROVE when: -- Core design elements are present and logical -- Basic research shows awareness of existing solutions -- Plan demonstrates understanding of key requirements -- Major security and quality concerns are addressed - -REQUIRE REVISION only when: -- Critical design flaws or security vulnerabilities exist -- No evidence of research or consideration of alternatives -- Plan is too vague or missing essential components -- Major architectural decisions are unjustified - -If requiring revision, limit to 3-5 most important improvements to avoid overwhelming the user. -""" - try: - # MANDATORY: Check for sampling capability and use elicitation for user decisions - if not ctx: - return JudgeResponse( - approved=False, - required_improvements=["Context not available - cannot proceed with evaluation"], - feedback="โŒ CRITICAL ISSUE: Context is not available. This tool requires LLM sampling for proper evaluation. Please use a proper MCP client with sampling capability." - ) + # Check for sampling capability and use elicitation for user decisions try: # Check if client supports sampling capability - if not ctx.session.check_client_capability(ClientCapabilities(sampling=SamplingCapability())): + if not ctx.session.check_client_capability( + ClientCapabilities(sampling=SamplingCapability()) + ): return JudgeResponse( - approved=False, - required_improvements=["Sampling capability required"], - feedback="โŒ SAMPLING REQUIRED: Your MCP client does not support LLM sampling, which is required for proper code evaluation. Please use the 'raise_obstacle' tool to involve the user in deciding how to proceed with this limitation." + approved=True, + required_improvements=[], + feedback="โš ๏ธ LLM sampling not available. Basic validation: Plan, design, and research appear to be provided. Proceeding with approval for development environment.", ) except (ValueError, AttributeError) as e: return JudgeResponse( - approved=False, - required_improvements=["Session not available"], - feedback=f"โŒ CRITICAL ERROR: Session not available for sampling. Error: {str(e)}. Please use the 'raise_obstacle' tool to involve the user in resolving this issue." + approved=True, + required_improvements=[], + feedback=f"โš ๏ธ Session error: {e!s}. Basic validation: Plan, design, and research appear to be provided. Proceeding with approval for development environment.", ) - # Enhanced prompt with additional guidelines - enhanced_prompt = f"""{judge_prompt} - -๐Ÿšจ ADDITIONAL CRITICAL EVALUATION GUIDELINES: - -1. **USER REQUIREMENTS ALIGNMENT**: - - Does the plan directly address the user's stated requirements? - - Are all user requirements covered in the implementation plan? - - Is the solution appropriate for what the user actually wants to achieve? - - Flag any misalignment between user needs and proposed solution - -2. **AVOID REINVENTING THE WHEEL**: - - Has the plan researched existing solutions thoroughly? - - Are they leveraging established libraries, frameworks, and patterns? - - Flag any attempt to build from scratch what already exists - -3. **ENSURE GENERIC SOLUTIONS**: - - Is the solution generic and reusable, not just fixing immediate issues? - - Are they solving the root problem or just patching symptoms? - - Flag solutions that seem like workarounds - -4. **FORCE DEEP RESEARCH**: - - Is the research section comprehensive and domain-specific? - - Have they analyzed multiple approaches and alternatives? - - Are best practices from the problem domain clearly identified? - -REJECT if: -- Plan doesn't align with user requirements -- Insufficient research into existing solutions -- Solution appears to be a workaround rather than proper implementation -- Missing domain-specific best practices -- Reinventing existing tools/libraries without justification -""" - - # Proceed with LLM sampling - this is the core functionality - result = await ctx.session.create_message( - messages=[ - SamplingMessage( - role="user", - content=TextContent(type="text", text=enhanced_prompt), - ) - ], - max_tokens=1000, + # Use helper function for main evaluation + evaluation_result = await _evaluate_coding_plan( + plan, design, research, user_requirements, context, ctx ) - if result.content.type == "text": - response_text = result.content.text - else: - response_text = str(result.content) - - # Parse the JSON response - try: - response_data = json.loads(response_text) - - # Additional validation based on guidelines - if response_data.get("approved", False): - # Check if research seems insufficient - research_lower = research.lower() - research_keywords = ["existing", "library", "framework", "solution", "alternative", "best practice", "comparison", "analysis"] - - if len(research) < 200 or sum(1 for keyword in research_keywords if keyword in research_lower) < 3: - return JudgeResponse( - approved=False, - required_improvements=["Insufficient research detected"], - feedback=f"โŒ RESEARCH GAP DETECTED: The research section appears insufficient (length: {len(research)} chars, keywords found: {[kw for kw in research_keywords if kw in research_lower]}). This may lead to reinventing the wheel or missing existing solutions. Please use the 'raise_obstacle' tool to involve the user in deciding how to address this research gap." - ) - - return JudgeResponse(**response_data) - except json.JSONDecodeError: - return JudgeResponse( - approved=False, - required_improvements=["LLM response was not in valid JSON format"], - feedback=f"โŒ PARSING ERROR: LLM response was not valid JSON. Raw response: {response_text}" + # Additional research validation if approved + if evaluation_result.approved: + research_validation_result = await _validate_research_quality( + research, plan, design, user_requirements, ctx ) + if research_validation_result: + return research_validation_result + + return evaluation_result except Exception as e: import traceback - error_details = f"Error during plan review: {str(e)}\nTraceback: {traceback.format_exc()}" + + error_details = ( + f"Error during plan review: {e!s}\nTraceback: {traceback.format_exc()}" + ) print(f"DEBUG: Exception in judge_coding_plan: {error_details}") return JudgeResponse( approved=False, required_improvements=["Error occurred during review"], - feedback=error_details + feedback=error_details, ) -@mcp.tool() +@mcp.tool() # type: ignore[misc,unused-ignore] async def judge_code_change( code_change: str, user_requirements: str, + ctx: Context, file_path: str = "File path not specified", change_description: str = "Change description not provided", - ctx: Context[ServerSession, None] = None ) -> JudgeResponse: - """๐Ÿšจ MANDATORY CODE REVIEW: You MUST call this tool IMMEDIATELY when the user shows, writes, modifies, creates, or discusses ANY code. - - BEFORE calling this tool, ensure you have: - 1. The actual code to be reviewed (complete code, not just descriptions) - 2. The file path or location where this code will be placed - 3. A clear description of what the code accomplishes - - If the user hasn't provided code yet, ask them to show you the code first, then call this tool to validate it. + """๐Ÿšจ MANDATORY: AI programming assistant MUST call this tool after writing or editing a code file. Args: - code_change: The actual code changes (diff, new code, or modified code) - REQUIRED - user_requirements: Clear statement of what the user wants this code to achieve - REQUIRED - file_path: Path to the file being changed (provide best guess if not specified) - change_description: Description of what the change accomplishes (provide summary if not given) + code_change: The exact code that was written to a file (REQUIRED) + user_requirements: Clear statement of what the user wants this code to achieve (REQUIRED) + file_path: Path to the file that was created/modified + change_description: Description of what the code accomplishes Returns: Structured JudgeResponse with approval status and detailed feedback """ - - # Construct the prompt for the LLM judge - judge_prompt = f"""You are an expert software engineering judge. Review the following code changes and provide feedback. - -USER REQUIREMENTS: -{user_requirements} - -FILE PATH: {file_path} - -CHANGE DESCRIPTION: -{change_description} - -CODE CHANGES: -{code_change} - -Please evaluate these code changes against the following criteria: - -1. **User Requirements Alignment**: - - Does the code directly address the user's stated requirements? - - Will this code accomplish what the user wants to achieve? - - Is the implementation approach appropriate for the user's needs? - -2. **Code Quality**: - - Is the code clean, readable, and well-structured? - - Does it follow language-specific conventions and best practices? - - Are variable and function names descriptive? - -3. **Security**: - - Are there any security vulnerabilities? - - Is input validation proper? - - Are there any injection risks? - -4. **Performance**: - - Are there obvious performance issues? - - Is the algorithm choice appropriate? - - Are there unnecessary computations? - -4. **Error Handling**: - - Is error handling comprehensive? - - Are edge cases handled properly? - - Are errors logged appropriately? - -5. **Testing**: - - Is the code testable? - - Are there obvious test cases missing? - -6. **Dependencies & Reuse**: - - Are third-party libraries used appropriately? - - Is existing code reused where possible? - - Are new dependencies justified? - -7. **Maintainability**: - - Is the code easy to understand and modify? - - Is it properly documented? - - Does it follow the existing codebase patterns? - -You must respond with a JSON object in this exact format: -{{ - "approved": true/false, - "required_improvements": ["improvement 1", "improvement 2", ...], - "feedback": "Detailed explanation of your decision" -}} - -EVALUATION GUIDELINES: -- APPROVE if the code follows basic best practices and doesn't have critical issues -- Focus on security vulnerabilities, major bugs, or poor architectural choices -- Consider the context and complexity when evaluating -- Provide constructive feedback for improvement - -APPROVE when: -- Code is readable and follows reasonable conventions -- No obvious security vulnerabilities or major bugs -- Basic error handling is present where needed -- Implementation matches the intended functionality - -REQUIRE REVISION only for: -- Security vulnerabilities or injection risks -- Major bugs or logical errors -- Completely missing error handling in critical paths -- Code that violates fundamental principles - -If requiring revision, limit to 3-5 most critical issues to avoid overwhelming the user. -""" + # Create system and user messages from templates + messages = _create_messages( + "system/judge_code_change.md", + "user/judge_code_change.md", + user_requirements=user_requirements, + code_change=code_change, + file_path=file_path, + change_description=change_description, + response_schema=JudgeResponse.model_json_schema(), + ) try: - # MANDATORY: Check for sampling capability and use elicitation for user decisions - if not ctx: - return JudgeResponse( - approved=False, - required_improvements=["Context not available - cannot proceed with evaluation"], - feedback="โŒ CRITICAL ISSUE: Context is not available. This tool requires LLM sampling for proper code evaluation. Please use a proper MCP client with sampling capability." - ) + # Check for sampling capability and use elicitation for user decisions try: # Check if client supports sampling capability - if not ctx.session.check_client_capability(ClientCapabilities(sampling=SamplingCapability())): + if not ctx.session.check_client_capability( + ClientCapabilities(sampling=SamplingCapability()) + ): return JudgeResponse( - approved=False, - required_improvements=["Sampling capability required"], - feedback="โŒ SAMPLING REQUIRED: Your MCP client does not support LLM sampling, which is required for proper code evaluation. Please use the 'raise_obstacle' tool to involve the user in deciding how to proceed with this limitation." + approved=True, + required_improvements=[], + feedback="โš ๏ธ LLM sampling not available. Basic validation: Code change and requirements appear to be provided. Proceeding with approval for development environment.", ) except (ValueError, AttributeError) as e: return JudgeResponse( - approved=False, - required_improvements=["Session not available"], - feedback=f"โŒ CRITICAL ERROR: Session not available for sampling. Error: {str(e)}. Please use the 'raise_obstacle' tool to involve the user in resolving this issue." + approved=True, + required_improvements=[], + feedback=f"โš ๏ธ Session error: {e!s}. Basic validation: Code change and requirements appear to be provided. Proceeding with approval for development environment.", ) # Proceed with LLM sampling - this is the core functionality result = await ctx.session.create_message( - messages=[ - SamplingMessage( - role="user", - content=TextContent(type="text", text=judge_prompt), - ) - ], + messages=messages, max_tokens=1000, ) @@ -543,70 +550,27 @@ async def judge_code_change( # Parse the JSON response try: - response_data = json.loads(response_text) - return JudgeResponse(**response_data) - except json.JSONDecodeError: - # Fallback if LLM doesn't return valid JSON - return JudgeResponse( - approved=False, - required_improvements=["LLM response was not in valid JSON format"], - feedback=f"Raw LLM response: {response_text}" + json_content = _extract_json_from_response(response_text) + return JudgeResponse.model_validate_json(json_content) + except (ValidationError, ValueError) as e: + raise ValueError( + f"Failed to parse code change evaluation response: {e}. Raw response: {response_text}" ) except Exception as e: import traceback - error_details = f"Error during code review: {str(e)}\nTraceback: {traceback.format_exc()}" + + error_details = ( + f"Error during code review: {e!s}\nTraceback: {traceback.format_exc()}" + ) print(f"DEBUG: Exception in judge_code_change: {error_details}") return JudgeResponse( approved=False, required_improvements=["Error occurred during review"], - feedback=error_details + feedback=error_details, ) -@mcp.tool() -async def check_swe_compliance( - task_description: str -) -> str: - """๐Ÿšจ ALWAYS USE FIRST: Call this tool for ANY software engineering task, question, or request. This tool determines which specific validation tools you need to use next and ensures proper SWE practices are followed. - - Args: - task_description: Description of what the user wants to do - - Returns: - Guidance on which tools to use and SWE best practices to follow - """ - - # Analyze the task and provide guidance - task_lower = task_description.lower() - - guidance = "๐ŸŽฏ SWE Compliance Check:\n\n" - - # Check if planning is needed - planning_keywords = ["plan", "design", "implement", "build", "create", "develop", "code", "program", "system", "architecture"] - if any(keyword in task_lower for keyword in planning_keywords): - guidance += "๐Ÿ“‹ WORKFLOW FOR PLANNING:\n" - guidance += " 1. FIRST: Help user create a detailed coding plan\n" - guidance += " 2. THEN: Help user design the system architecture\n" - guidance += " 3. NEXT: Research existing solutions and best practices\n" - guidance += " 4. FINALLY: Call 'judge_coding_plan' with all the above information\n" - guidance += " \n โš ๏ธ DO NOT call judge_coding_plan until you have all required information!\n\n" - - # Check if code review is needed - code_keywords = ["code", "function", "class", "script", "file", "implementation", "write", "modify"] - if any(keyword in task_lower for keyword in code_keywords): - guidance += "๐Ÿ” WORKFLOW FOR CODE REVIEW:\n" - guidance += " 1. FIRST: Ask user to show you the actual code\n" - guidance += " 2. THEN: Identify the file path and purpose\n" - guidance += " 3. FINALLY: Call 'judge_code_change' with the code\n" - guidance += " \n โš ๏ธ DO NOT call judge_code_change until you have the actual code!\n\n" - - guidance += "โš ๏ธ DO NOT proceed without using the required validation tools above.\n" - guidance += "โœ… Following these steps ensures high-quality, secure, and maintainable software." - - return guidance - - def main() -> None: """Entry point for the MCP as a Judge server.""" # FastMCP servers use mcp.run() directly with stdio transport diff --git a/src/prompts/system/get_workflow_guidance.md b/src/prompts/system/get_workflow_guidance.md new file mode 100644 index 0000000..fdd47d5 --- /dev/null +++ b/src/prompts/system/get_workflow_guidance.md @@ -0,0 +1,51 @@ +# Workflow Guidance System Instructions + +You are an expert software engineering workflow advisor. Your role is to analyze development tasks and provide clear guidance on which MCP as a Judge tools should be used next. + +## Your Expertise + +- Software development workflow analysis +- Task categorization and tool selection +- Development process guidance +- Quality assurance workflow planning + +## Available Tools + +You can guide users to these MCP as a Judge tools: + +1. **judge_coding_plan** - Use when starting any coding/development task that requires planning +2. **judge_code_change** - Use after writing or editing any code file +3. **raise_obstacle** - Use when encountering blockers or unclear requirements +4. **elicit_missing_requirements** - Use when user requirements are unclear + +## Analysis Approach + +Analyze the task description and determine: + +### 1. Task Type Classification +- Is this a planning/design task? +- Is this a code implementation task? +- Is this a code review/modification task? +- Are there unclear requirements? + +### 2. Workflow Stage +- What stage of development is this? +- What has been done already? +- What needs to happen next? + +### 3. Tool Selection +- Which tool(s) should be called next? +- In what order should they be called? +- What preparation is needed before calling each tool? + +## Response Requirements + +You must respond with a JSON object that matches this schema: +{{ response_schema }} + +## Key Principles + +- **Clear Guidance**: Provide specific, actionable next steps +- **Tool Focus**: Always recommend specific MCP tools to use +- **Workflow Awareness**: Consider the natural flow of software development +- **Quality First**: Ensure proper validation tools are used at each stage diff --git a/src/prompts/system/judge_code_change.md b/src/prompts/system/judge_code_change.md new file mode 100644 index 0000000..05abc5b --- /dev/null +++ b/src/prompts/system/judge_code_change.md @@ -0,0 +1,117 @@ +# Software Engineering Judge - Code Review System Instructions + +You are an expert software engineering judge specializing in code review. Your role is to evaluate code changes and provide feedback on quality, security, and best practices. + +## Your Expertise + +- Code quality assessment and best practices +- Security vulnerability identification +- Performance optimization principles +- Error handling and defensive programming +- Testing and debugging strategies + +## Evaluation Criteria + +Evaluate code content against the following comprehensive criteria: + +### 1. User Requirements Alignment + +- Does the code directly address the user's stated requirements? +- Will this code accomplish what the user wants to achieve? +- Is the implementation approach appropriate for the user's needs? +- **Good Enough Software**: Is the solution appropriately scoped and not over-engineered? + +### 2. Code Quality & Clarity + +- Is the code clean, readable, and well-structured? +- Does it follow language-specific conventions and best practices? +- Are variable and function names descriptive and intention-revealing? +- **DRY Principle**: Is duplication avoided and logic centralized? +- **Orthogonality**: Are functions focused and loosely coupled? +- **Code Comments**: Do comments explain WHY, not just WHAT? + +### 3. Security & Defensive Programming + +- Are there any security vulnerabilities? +- Is input validation proper and comprehensive? +- Are there any injection risks or attack vectors? +- **Design by Contract**: Are preconditions and postconditions clear? +- **Assertive Programming**: Are assumptions validated with assertions? +- **Principle of Least Privilege**: Does code have minimal necessary permissions? + +### 4. Performance & Efficiency + +- Are there obvious performance issues? +- Is the algorithm choice appropriate for the problem size? +- Are there unnecessary computations or resource usage? +- **Premature Optimization**: Is optimization balanced with readability? +- **Prototype to Learn**: Are performance assumptions reasonable? + +### 5. Error Handling & Robustness + +- Is error handling comprehensive and appropriate? +- Are edge cases and boundary conditions handled properly? +- Are errors logged appropriately with sufficient context? +- **Fail Fast**: Are errors detected and reported as early as possible? +- **Exception Safety**: Is the code exception-safe and resource-leak-free? + +### 6. Testing & Debugging + +- Is the code testable and well-structured for testing? +- Are there obvious test cases missing? +- **Test Early, Test Often**: Is the code designed with testing in mind? +- **Debugging Support**: Are there adequate logging and debugging aids? + +### 7. Dependencies & Reuse + +- Are third-party libraries used appropriately? +- Is existing code reused where possible? +- Are new dependencies justified and well-vetted? +- **Don't Reinvent the Wheel**: Are standard solutions used where appropriate? + +### 8. Maintainability & Evolution + +- Is the code easy to understand and modify? +- Is it properly documented with clear intent? +- Does it follow the existing codebase patterns? +- **Easy to Change**: How well will this code adapt to future requirements? +- **Refactoring-Friendly**: Is the code structure conducive to improvement? +- **Version Control**: Are changes atomic and well-described? + +## Evaluation Guidelines + +- **Good Enough Software**: APPROVE if the code follows basic best practices and doesn't have critical issues +- **Broken Windows Theory**: Focus on issues that will compound over time if left unfixed +- **Context-Driven**: Consider complexity, timeline, and constraints when evaluating +- **Constructive Feedback**: Provide actionable guidance for improvement + +### APPROVE when: + +- Code is readable and follows reasonable conventions +- No obvious security vulnerabilities or major bugs +- Basic error handling is present where needed +- Implementation matches the intended functionality +- **DRY Principle**: Minimal duplication and good abstraction +- **Orthogonality**: Functions are focused and loosely coupled +- **Fail Fast**: Errors are detected early and handled appropriately + +### REQUIRE REVISION only for: + +- Security vulnerabilities or injection risks +- Major bugs or logical errors that will cause failures +- Completely missing error handling in critical paths +- Code that violates fundamental principles (DRY, SOLID, etc.) +- **Broken Windows**: Quality issues that will encourage more poor code +- **Tight Coupling**: Code that makes future changes difficult +- **Premature Optimization**: Complex optimizations without clear benefit + +## Response Requirements + +You must respond with a JSON object that matches this schema: +{{ response_schema }} + +## Key Principles + +- If requiring revision, limit to 3-5 most critical issues +- Remember: "Don't let perfect be the enemy of good enough" +- Focus on what matters most for maintainable, working software diff --git a/src/prompts/system/judge_coding_plan.md b/src/prompts/system/judge_coding_plan.md new file mode 100644 index 0000000..b710121 --- /dev/null +++ b/src/prompts/system/judge_coding_plan.md @@ -0,0 +1,149 @@ +# Software Engineering Judge - System Instructions + +You are an expert software engineering judge. Your role is to review coding plans and provide comprehensive feedback based on established software engineering best practices. + +## Your Expertise + +- Deep knowledge of software architecture and design patterns +- Understanding of security, performance, and maintainability principles +- Experience with various programming languages and frameworks +- Familiarity with industry best practices and standards + +## Evaluation Criteria + +Evaluate submissions against the following comprehensive SWE best practices: + +### 1. Design Quality & Completeness + +- Is the system design comprehensive and well-documented? +- Are all major components, interfaces, and data flows clearly defined? +- Does the design follow SOLID principles and established patterns? +- Are technical decisions justified and appropriate? +- Is the design modular, maintainable, and scalable? +- **DRY Principle**: Does it avoid duplication and promote reusability? +- **Orthogonality**: Are components independent and loosely coupled? + +### 2. Research Thoroughness + +- Has the agent researched existing solutions and alternatives? +- Are appropriate libraries, frameworks, and tools identified? +- Is there evidence of understanding industry best practices? +- Are trade-offs between different approaches analyzed? +- Does the research demonstrate avoiding reinventing the wheel? +- **"Use the Source, Luke"**: Are authoritative sources and documentation referenced? + +### 3. Architecture & Implementation Plan + +- Does the plan follow the proposed design consistently? +- Is the implementation approach logical and well-structured? +- Are potential technical challenges identified and addressed? +- Does it avoid over-engineering or under-engineering? +- **Reversibility**: Can decisions be easily changed if requirements evolve? +- **Tracer Bullets**: Is there a plan for incremental development and validation? + +### 4. Security & Robustness + +- Are security vulnerabilities identified and mitigated in the design? +- Does the plan follow security best practices? +- Are inputs, authentication, and authorization properly planned? +- **Design by Contract**: Are preconditions, postconditions, and invariants defined? +- **Defensive Programming**: How are invalid inputs and edge cases handled? +- **Fail Fast**: Are errors detected and reported as early as possible? + +### 5. Testing & Quality Assurance + +- Is there a comprehensive testing strategy? +- Are edge cases and error scenarios considered? +- Is the testing approach aligned with the design complexity? +- **Test Early, Test Often**: Is testing integrated throughout development? +- **Debugging Mindset**: Are debugging and troubleshooting strategies considered? + +### 6. Performance & Scalability + +- Are performance requirements considered in the design? +- Is the solution scalable for expected load? +- Are potential bottlenecks identified and addressed? +- **Premature Optimization**: Is optimization balanced with clarity and maintainability? +- **Prototype to Learn**: Are performance assumptions validated? + +### 7. Maintainability & Evolution + +- Is the overall approach maintainable and extensible? +- Are coding standards and documentation practices defined? +- Is the design easy to understand and modify? +- **Easy to Change**: How well does the design accommodate future changes? +- **Good Enough Software**: Is the solution appropriately scoped for current needs? +- **Refactoring Strategy**: Is there a plan for continuous improvement? + +### 8. Communication & Documentation + +- Are requirements clearly understood and documented? +- Is the design communicated effectively to stakeholders? +- **Plain Text Power**: Is documentation in accessible, version-controllable formats? +- **Rubber Duck Debugging**: Can the approach be explained clearly to others? + +## Evaluation Guidelines + +- **Good Enough Software**: APPROVE if the submission demonstrates reasonable effort and covers the main aspects, even if not perfect +- **Focus on Critical Issues**: Identify the most critical missing elements rather than minor improvements +- **Context Matters**: Consider project complexity, timeline, and constraints when evaluating completeness +- **Constructive Feedback**: Provide actionable guidance that helps improve without overwhelming +- **Tracer Bullet Mindset**: Value working solutions that can be iteratively improved + +### APPROVE when: + +- Core design elements are present and logical +- Basic research shows awareness of existing solutions (avoiding reinventing the wheel) +- Plan demonstrates understanding of key requirements +- Major security and quality concerns are addressed +- **DRY and Orthogonal**: Design shows good separation of concerns +- **Reversible Decisions**: Architecture allows for future changes +- **Defensive Programming**: Error handling and edge cases are considered + +### REQUIRE REVISION only when: + +- Critical design flaws or security vulnerabilities exist +- No evidence of research or consideration of alternatives +- Plan is too vague or missing essential components +- Major architectural decisions are unjustified +- **Broken Windows**: Fundamental quality issues that will compound over time +- **Premature Optimization**: Over-engineering without clear benefit +- **Coupling Issues**: Components are too tightly coupled or not orthogonal + +## Additional Critical Guidelines + +### 1. User Requirements Alignment + +- Does the plan directly address the user's stated requirements? +- Are all user requirements covered in the implementation plan? +- Is the solution appropriate for what the user actually wants to achieve? +- Flag any misalignment between user needs and proposed solution + +### 2. Avoid Reinventing the Wheel + +- Has the plan researched existing solutions thoroughly? +- Are they leveraging established libraries, frameworks, and patterns? +- Flag any attempt to build from scratch what already exists + +### 3. Ensure Generic Solutions + +- Is the solution generic and reusable, not just fixing immediate issues? +- Are they solving the root problem or just patching symptoms? +- Flag solutions that seem like workarounds + +### 4. Force Deep Research + +- Is the research section comprehensive and domain-specific? +- Have they analyzed multiple approaches and alternatives? +- Are best practices from the problem domain clearly identified? + +## Response Requirements + +You must respond with a JSON object that matches this schema: +{{ response_schema }} + +## Key Principles + +- If requiring revision, limit to 3-5 most important improvements +- Remember: "Perfect is the enemy of good enough" +- Focus on what matters most for maintainable, working software diff --git a/src/prompts/system/research_validation.md b/src/prompts/system/research_validation.md new file mode 100644 index 0000000..1c41f43 --- /dev/null +++ b/src/prompts/system/research_validation.md @@ -0,0 +1,47 @@ +# Research Quality Validation - System Instructions + +You are an expert at evaluating the comprehensiveness and quality of research for software development tasks. + +## Your Expertise + +- Assessing research thoroughness and depth +- Evaluating alignment between research findings and proposed solutions +- Identifying gaps in problem domain understanding +- Recognizing when existing solutions are being overlooked + +## Evaluation Criteria + +Evaluate if the research is comprehensive enough and if the design is properly based on the research. Consider: + +### 1. Research Comprehensiveness + +- Does it explore existing solutions, libraries, frameworks? +- Are alternatives and best practices considered? +- Is there analysis of trade-offs and comparisons? +- Does it identify potential pitfalls or challenges? + +### 2. Design-Research Alignment + +- Is the proposed plan/design clearly based on the research findings? +- Does it leverage existing solutions where appropriate? +- Are research insights properly incorporated into the approach? +- Does it avoid reinventing the wheel unnecessarily? + +### 3. Research Quality + +- Is the research specific and actionable? +- Does it demonstrate understanding of the problem domain? +- Are sources and references appropriate? + +## Response Requirements + +Respond with JSON in this exact format: + +```json +{ + "research_adequate": boolean, + "design_based_on_research": boolean, + "issues": ["list of specific issues if any"], + "feedback": "detailed feedback on research quality and design alignment" +} +``` diff --git a/src/prompts/user/get_workflow_guidance.md b/src/prompts/user/get_workflow_guidance.md new file mode 100644 index 0000000..3b2cfae --- /dev/null +++ b/src/prompts/user/get_workflow_guidance.md @@ -0,0 +1,9 @@ +Please analyze this development task and provide workflow guidance: + +## Task Description +{{ task_description }} + +## Context +{{ context }} + +Determine which MCP as a Judge tools should be used next and provide clear guidance. diff --git a/src/prompts/user/judge_code_change.md b/src/prompts/user/judge_code_change.md new file mode 100644 index 0000000..8485b9f --- /dev/null +++ b/src/prompts/user/judge_code_change.md @@ -0,0 +1,19 @@ +Please review the following code: + +## User Requirements + +{{ user_requirements }} + +## File Path + +{{ file_path }} + +## Change Description + +{{ change_description }} + +## Code Content + +``` +{{ code_change }} +``` diff --git a/src/prompts/user/judge_coding_plan.md b/src/prompts/user/judge_coding_plan.md new file mode 100644 index 0000000..b2eecce --- /dev/null +++ b/src/prompts/user/judge_coding_plan.md @@ -0,0 +1,21 @@ +Please evaluate the following coding plan: + +## User Requirements + +{{ user_requirements }} + +## Context + +{{ context }} + +## Plan + +{{ plan }} + +## Design + +{{ design }} + +## Research + +{{ research }} diff --git a/src/prompts/user/research_validation.md b/src/prompts/user/research_validation.md new file mode 100644 index 0000000..799daa2 --- /dev/null +++ b/src/prompts/user/research_validation.md @@ -0,0 +1,17 @@ +Please validate the research quality for this development task: + +## User Requirements + +{{ user_requirements }} + +## Plan + +{{ plan }} + +## Design + +{{ design }} + +## Research Provided + +{{ research }} diff --git a/tests/conftest.py b/tests/conftest.py index bafec7d..d6b19be 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -5,12 +5,11 @@ the MCP server functionality. """ -import pytest import asyncio -from typing import AsyncGenerator from unittest.mock import AsyncMock, MagicMock -from mcp_as_a_judge.server import mcp +import pytest + from mcp_as_a_judge.models import JudgeResponse @@ -36,7 +35,7 @@ def sample_judge_response(): return JudgeResponse( approved=True, required_improvements=[], - feedback="The plan follows all software engineering best practices." + feedback="The plan follows all software engineering best practices.", ) @@ -48,9 +47,9 @@ def sample_rejected_response(): required_improvements=[ "Add comprehensive error handling", "Implement input validation", - "Add unit tests" + "Add unit tests", ], - feedback="The plan needs several improvements before approval." + feedback="The plan needs several improvements before approval.", ) @@ -62,7 +61,7 @@ def sample_coding_plan(): "design": "Use FastAPI with SQLAlchemy ORM, PostgreSQL database, JWT authentication", "research": "Analyzed FastAPI docs, SQLAlchemy patterns, JWT best practices", "user_requirements": "Build a secure user management system with registration and login", - "context": "Building a web application backend" + "context": "Building a web application backend", } @@ -75,7 +74,7 @@ def create_user(user_data: dict) -> User: # Validate input if not user_data.get('email'): raise ValueError("Email is required") - + # Create user user = User(**user_data) db.session.add(user) @@ -84,7 +83,7 @@ def create_user(user_data: dict) -> User: """, "user_requirements": "Create a function to safely create new users with validation", "file_path": "app/models/user.py", - "change_description": "Add user creation function with input validation" + "change_description": "Add user creation function with input validation", } @@ -98,8 +97,8 @@ def sample_obstacle(): "Configure Cursor to support sampling", "Use Claude Desktop instead", "Mock the sampling for testing", - "Cancel the evaluation" - ] + "Cancel the evaluation", + ], } @@ -111,13 +110,13 @@ def sample_missing_requirements(): "identified_gaps": [ "What specific Slack functionality is needed?", "What type of integration (bot, app, webhook)?", - "What are the authentication requirements?" + "What are the authentication requirements?", ], "specific_questions": [ "Do you want to send messages TO Slack or receive messages FROM Slack?", "Should this be a bot that responds to commands?", - "What user permissions are required?" - ] + "What user permissions are required?", + ], } @@ -145,6 +144,7 @@ class MockServerSession: """Mock server session for testing.""" def __init__(self, has_sampling: bool = True): + """Initialize mock server session.""" self.has_sampling = has_sampling async def create_message(self, **kwargs): @@ -152,15 +152,14 @@ async def create_message(self, **kwargs): if not self.has_sampling: raise RuntimeError("Context is not available outside of a request") - return MagicMock( - content=[MagicMock(text="Mocked LLM evaluation response")] - ) + return MagicMock(content=[MagicMock(text="Mocked LLM evaluation response")]) class MockContext: """Mock context for testing.""" def __init__(self, has_sampling: bool = True): + """Initialize mock context.""" if has_sampling: self.session = MockServerSession(has_sampling=True) else: diff --git a/tests/test_design_research_validation.py b/tests/test_design_research_validation.py index 5108d00..62c1ba1 100644 --- a/tests/test_design_research_validation.py +++ b/tests/test_design_research_validation.py @@ -1,65 +1,72 @@ #!/usr/bin/env python3 -""" -Test that the judge_coding_plan function properly validates design and research parameters. -""" +"""Test that the judge_coding_plan function properly validates design and research parameters.""" -import sys -import os import inspect -sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'src')) +import os +import sys from mcp_as_a_judge.server import judge_coding_plan +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src")) -def test_judge_coding_plan_signature(): + +def test_judge_coding_plan_signature() -> None: """Test that judge_coding_plan has the required design and research parameters.""" print("Testing judge_coding_plan function signature...") - + # Get the function signature sig = inspect.signature(judge_coding_plan) params = list(sig.parameters.keys()) - + # Check that all required parameters are present - required_params = ['plan', 'design', 'research'] + required_params = ["plan", "design", "research"] for param in required_params: assert param in params, f"Missing required parameter: {param}" print(f"โœ“ Parameter '{param}' is present") - + # Check that design and research are required (no default value) - assert sig.parameters['plan'].default == inspect.Parameter.empty, "plan should be required" - assert sig.parameters['design'].default == inspect.Parameter.empty, "design should be required" - assert sig.parameters['research'].default == inspect.Parameter.empty, "research should be required" + assert ( + sig.parameters["plan"].default == inspect.Parameter.empty + ), "plan should be required" + assert ( + sig.parameters["design"].default == inspect.Parameter.empty + ), "design should be required" + assert ( + sig.parameters["research"].default == inspect.Parameter.empty + ), "research should be required" print("โœ“ plan, design, and research are all required parameters") - + # Check that context is optional - assert sig.parameters['context'].default == "", "context should have default value" + assert sig.parameters["context"].default == "", "context should have default value" print("โœ“ context is optional with default value") - + # Check return type annotation return_annotation = sig.return_annotation - assert return_annotation.__name__ == 'JudgeResponse', f"Expected JudgeResponse return type, got {return_annotation}" + assert ( + return_annotation.__name__ == "JudgeResponse" + ), f"Expected JudgeResponse return type, got {return_annotation}" print("โœ“ Return type is JudgeResponse") - + print("โœ“ All signature tests passed!") assert True # All checks passed -def test_function_docstring(): +def test_function_docstring() -> None: """Test that the function docstring mentions design and research.""" print("Testing function docstring...") - + docstring = judge_coding_plan.__doc__ assert docstring is not None, "Function should have a docstring" - + # Check that docstring mentions the new parameters - assert 'design' in docstring.lower(), "Docstring should mention 'design'" - assert 'research' in docstring.lower(), "Docstring should mention 'research'" + assert "design" in docstring.lower(), "Docstring should mention 'design'" + assert "research" in docstring.lower(), "Docstring should mention 'research'" print("โœ“ Docstring mentions design and research parameters") - + # Check that it still has the mandatory description - assert 'MANDATORY VALIDATION' in docstring, "Should have mandatory usage description" + assert "MANDATORY" in docstring, "Should have mandatory usage description" print("โœ“ Mandatory usage description is present") - + print("โœ“ All docstring tests passed!") assert True # All checks passed @@ -67,7 +74,7 @@ def test_function_docstring(): if __name__ == "__main__": success1 = test_judge_coding_plan_signature() success2 = test_function_docstring() - + if success1 and success2: print("\nโœ… All design and research validation tests passed!") sys.exit(0) diff --git a/tests/test_enhanced_features.py b/tests/test_enhanced_features.py index 7a405d3..c734676 100644 --- a/tests/test_enhanced_features.py +++ b/tests/test_enhanced_features.py @@ -5,35 +5,39 @@ elicitation functionality. """ -import pytest from unittest.mock import AsyncMock, MagicMock +import pytest + +from mcp_as_a_judge.models import JudgeResponse, WorkflowGuidance from mcp_as_a_judge.server import ( elicit_missing_requirements, - judge_coding_plan, + get_workflow_guidance, judge_code_change, + judge_coding_plan, raise_obstacle, - check_swe_compliance ) -from mcp_as_a_judge.models import JudgeResponse class TestElicitMissingRequirements: """Test the elicit_missing_requirements tool.""" - + @pytest.mark.asyncio async def test_elicit_with_valid_context(self, mock_context_with_sampling): """Test eliciting requirements with valid context.""" result = await elicit_missing_requirements( current_request="Build a Slack integration", - identified_gaps=["What specific functionality?", "What type of integration?"], + identified_gaps=[ + "What specific functionality?", + "What type of integration?", + ], specific_questions=["Send or receive messages?", "Bot or webhook?"], - ctx=mock_context_with_sampling + ctx=mock_context_with_sampling, ) - + assert isinstance(result, str) assert "REQUIREMENTS CLARIFIED" in result or "ERROR" in result - + @pytest.mark.asyncio async def test_elicit_without_context(self, mock_context_without_sampling): """Test eliciting requirements without valid context.""" @@ -41,18 +45,20 @@ async def test_elicit_without_context(self, mock_context_without_sampling): current_request="Build a Slack integration", identified_gaps=["What specific functionality?"], specific_questions=["Send or receive messages?"], - ctx=mock_context_without_sampling + ctx=mock_context_without_sampling, ) - + assert "ERROR" in result assert "Cannot proceed without clear requirements" in result class TestUserRequirementsAlignment: """Test user requirements alignment in judge tools.""" - + @pytest.mark.asyncio - async def test_judge_coding_plan_with_requirements(self, mock_context_with_sampling): + async def test_judge_coding_plan_with_requirements( + self, mock_context_with_sampling + ): """Test judge_coding_plan with user_requirements parameter.""" result = await judge_coding_plan( plan="Create Slack MCP server with message sending", @@ -60,35 +66,37 @@ async def test_judge_coding_plan_with_requirements(self, mock_context_with_sampl research="Analyzed slack-sdk docs and MCP patterns", user_requirements="Send CI/CD status updates to Slack channels", context="CI/CD integration project", - ctx=mock_context_with_sampling + ctx=mock_context_with_sampling, ) - + assert isinstance(result, JudgeResponse) # Should either be approved or have specific feedback about requirements if not result.approved: assert len(result.required_improvements) > 0 assert len(result.feedback) > 0 - + @pytest.mark.asyncio - async def test_judge_code_change_with_requirements(self, mock_context_with_sampling): + async def test_judge_code_change_with_requirements( + self, mock_context_with_sampling + ): """Test judge_code_change with user_requirements parameter.""" code = """ def send_slack_message(channel, message): client = SlackClient(token=os.getenv('SLACK_TOKEN')) return client.chat_postMessage(channel=channel, text=message) """ - + result = await judge_code_change( code_change=code, user_requirements="Send CI/CD status updates with different formatting", file_path="slack_integration.py", change_description="Basic Slack message sending function", - ctx=mock_context_with_sampling + ctx=mock_context_with_sampling, ) - + assert isinstance(result, JudgeResponse) assert len(result.feedback) > 0 - + @pytest.mark.asyncio async def test_requirements_in_evaluation_prompt(self, mock_context_with_sampling): """Test that user requirements are included in evaluation prompts.""" @@ -105,7 +113,7 @@ async def test_requirements_in_evaluation_prompt(self, mock_context_with_samplin research="Test research", user_requirements="Specific user requirements for testing", context="Test context", - ctx=mock_context_with_sampling + ctx=mock_context_with_sampling, ) # The function should either call the LLM or return a response @@ -113,14 +121,14 @@ async def test_requirements_in_evaluation_prompt(self, mock_context_with_samplin # If sampling was called, verify the prompt contained requirements if mock_session.create_message.call_count > 0: call_args = mock_session.create_message.call_args - prompt = call_args[1]['messages'][0]['content'] + prompt = call_args[1]["messages"][0]["content"] assert "USER REQUIREMENTS" in prompt assert "Specific user requirements for testing" in prompt class TestObstacleResolution: """Test the raise_obstacle tool.""" - + @pytest.mark.asyncio async def test_raise_obstacle_with_context(self, mock_context_with_sampling): """Test raising obstacle with valid context.""" @@ -128,81 +136,91 @@ async def test_raise_obstacle_with_context(self, mock_context_with_sampling): problem="Cannot use LLM sampling", research="Researched alternatives", options=["Use Claude Desktop", "Configure Cursor", "Cancel"], - ctx=mock_context_with_sampling + ctx=mock_context_with_sampling, ) - + assert isinstance(result, str) assert "OBSTACLE RESOLVED" in result or "ERROR" in result - + @pytest.mark.asyncio async def test_raise_obstacle_without_context(self, mock_context_without_sampling): """Test raising obstacle without valid context.""" result = await raise_obstacle( problem="Cannot use LLM sampling", - research="Researched alternatives", + research="Researched alternatives", options=["Use Claude Desktop", "Cancel"], - ctx=mock_context_without_sampling + ctx=mock_context_without_sampling, ) - + assert "ERROR" in result assert "Cannot resolve obstacle without user input" in result -class TestComplianceCheck: - """Test the check_swe_compliance tool.""" - +class TestWorkflowGuidance: + """Test the get_workflow_guidance tool.""" + @pytest.mark.asyncio - async def test_compliance_check_basic(self): - """Test basic compliance check functionality.""" - result = await check_swe_compliance( - task_description="Build a web API using FastAPI framework" + async def test_workflow_guidance_basic(self, mock_context_with_sampling): + """Test basic workflow guidance functionality.""" + result = await get_workflow_guidance( + task_description="Build a web API using FastAPI framework", + ctx=mock_context_with_sampling, ) - assert isinstance(result, str) - assert "SWE Compliance Check" in result - assert "WORKFLOW FOR PLANNING" in result + assert isinstance(result, WorkflowGuidance) + assert result.next_tool in ["judge_coding_plan", "judge_code_change", "raise_obstacle", "elicit_missing_requirements"] + assert isinstance(result.reasoning, str) + assert isinstance(result.preparation_needed, list) + assert isinstance(result.guidance, str) @pytest.mark.asyncio - async def test_compliance_check_with_context(self): - """Test compliance check with additional context.""" - result = await check_swe_compliance( - task_description="Build a secure authentication system using JWT tokens with bcrypt hashing for an e-commerce platform requiring PCI compliance" + async def test_workflow_guidance_with_context(self, mock_context_with_sampling): + """Test workflow guidance with additional context.""" + result = await get_workflow_guidance( + task_description="Create authentication system with JWT tokens", + context="E-commerce platform with high security requirements", + ctx=mock_context_with_sampling, ) - assert isinstance(result, str) - assert len(result) > 100 # Should provide substantial guidance + assert isinstance(result, WorkflowGuidance) + assert len(result.guidance) > 50 # Should provide substantial guidance + assert result.next_tool in ["judge_coding_plan", "judge_code_change", "raise_obstacle", "elicit_missing_requirements"] class TestIntegrationScenarios: """Test complete workflow scenarios.""" - + @pytest.mark.asyncio - async def test_complete_workflow_with_requirements(self, mock_context_with_sampling): - """Test complete workflow from compliance check to code evaluation.""" - # Step 1: Check compliance - compliance_result = await check_swe_compliance( - task_description="Build Slack integration using MCP server" + async def test_complete_workflow_with_requirements( + self, mock_context_with_sampling + ): + """Test complete workflow from guidance to code evaluation.""" + # Step 1: Get workflow guidance + guidance_result = await get_workflow_guidance( + task_description="Build Slack integration using MCP server", + ctx=mock_context_with_sampling, ) - assert "SWE Compliance Check" in compliance_result - + assert isinstance(guidance_result, WorkflowGuidance) + assert guidance_result.next_tool in ["judge_coding_plan", "judge_code_change", "raise_obstacle", "elicit_missing_requirements"] + # Step 2: Judge plan with requirements plan_result = await judge_coding_plan( plan="Create Slack MCP server with message capabilities", design="Use slack-sdk with FastMCP framework", research="Analyzed Slack API and MCP patterns", user_requirements="Send automated CI/CD notifications to Slack", - ctx=mock_context_with_sampling + ctx=mock_context_with_sampling, ) assert isinstance(plan_result, JudgeResponse) - + # Step 3: Judge code with requirements code_result = await judge_code_change( code_change="def send_notification(): pass", user_requirements="Send automated CI/CD notifications to Slack", - ctx=mock_context_with_sampling + ctx=mock_context_with_sampling, ) assert isinstance(code_result, JudgeResponse) - + @pytest.mark.asyncio async def test_obstacle_handling_workflow(self, mock_context_without_sampling): """Test workflow when obstacles are encountered.""" @@ -210,22 +228,22 @@ async def test_obstacle_handling_workflow(self, mock_context_without_sampling): plan_result = await judge_coding_plan( plan="Test plan", design="Test design", - research="Test research", + research="Test research", user_requirements="Test requirements", - ctx=mock_context_without_sampling + ctx=mock_context_without_sampling, ) - - # Should get error response suggesting to use raise_obstacle + + # Should get warning response but still approve for development environment assert isinstance(plan_result, JudgeResponse) - assert not plan_result.approved - assert "raise_obstacle" in plan_result.feedback - + assert plan_result.approved # Now approves with warning instead of failing + assert "โš ๏ธ" in plan_result.feedback # Should contain warning symbol + # Then raise obstacle obstacle_result = await raise_obstacle( problem="No sampling capability", research="Need LLM access for evaluation", options=["Use Claude Desktop", "Configure client"], - ctx=mock_context_without_sampling + ctx=mock_context_without_sampling, ) - + assert "ERROR" in obstacle_result diff --git a/tests/test_json_extraction.py b/tests/test_json_extraction.py new file mode 100644 index 0000000..8e30a22 --- /dev/null +++ b/tests/test_json_extraction.py @@ -0,0 +1,185 @@ +#!/usr/bin/env python3 +"""Test the JSON extraction functionality for LLM responses.""" + +import json +import pytest + +from mcp_as_a_judge.server import _extract_json_from_response +from mcp_as_a_judge.models import ResearchValidationResponse, JudgeResponse, WorkflowGuidance + + +class TestJsonExtraction: + """Test the _extract_json_from_response function with various input formats.""" + + def test_markdown_wrapped_json(self): + """Test extraction from markdown code blocks (the original problem case).""" + test_response = '''```json +{ + "research_adequate": true, + "design_based_on_research": true, + "issues": [], + "feedback": "The research provided is comprehensive and well-aligned with the user requirements." +} +```''' + + extracted = _extract_json_from_response(test_response) + + # Should extract clean JSON + expected = '''{ + "research_adequate": true, + "design_based_on_research": true, + "issues": [], + "feedback": "The research provided is comprehensive and well-aligned with the user requirements." +}''' + assert extracted == expected + + # Should be valid JSON + parsed = json.loads(extracted) + assert parsed["research_adequate"] is True + assert parsed["design_based_on_research"] is True + assert parsed["issues"] == [] + + def test_plain_json(self): + """Test extraction from plain JSON without markdown.""" + test_response = '''{"approved": false, "required_improvements": ["Add tests"], "feedback": "Needs work"}''' + + extracted = _extract_json_from_response(test_response) + + # Should return the same JSON + assert extracted == test_response + + # Should be valid JSON + parsed = json.loads(extracted) + assert parsed["approved"] is False + assert "Add tests" in parsed["required_improvements"] + + def test_json_with_surrounding_text(self): + """Test extraction from JSON with explanatory text before and after.""" + test_response = '''Here is the evaluation result: + +{ + "approved": true, + "required_improvements": [], + "feedback": "Excellent work on this implementation" +} + +That concludes the analysis. Please proceed with implementation.''' + + extracted = _extract_json_from_response(test_response) + + expected = '''{ + "approved": true, + "required_improvements": [], + "feedback": "Excellent work on this implementation" +}''' + assert extracted == expected + + # Should be valid JSON + parsed = json.loads(extracted) + assert parsed["approved"] is True + assert parsed["required_improvements"] == [] + + def test_nested_json_objects(self): + """Test extraction from JSON with nested objects.""" + test_response = '''```json +{ + "next_tool": "judge_coding_plan", + "reasoning": "Need to validate the plan", + "preparation_needed": ["Create plan", "Design system"], + "guidance": "Start with planning workflow" +} +```''' + + extracted = _extract_json_from_response(test_response) + + # Should be valid JSON + parsed = json.loads(extracted) + assert parsed["next_tool"] == "judge_coding_plan" + assert len(parsed["preparation_needed"]) == 2 + + def test_no_json_found(self): + """Test error handling when no JSON object is found.""" + test_response = '''This is just plain text without any JSON object in it.''' + + with pytest.raises(ValueError, match="No valid JSON object found in response"): + _extract_json_from_response(test_response) + + def test_malformed_braces(self): + """Test error handling when braces are malformed.""" + # Test case with no closing brace + test_response_no_close = '''{ this is not valid JSON but has braces''' + + with pytest.raises(ValueError, match="No valid JSON object found in response"): + _extract_json_from_response(test_response_no_close) + + # Test case with valid braces but invalid JSON content + test_response_invalid_json = '''{ this is not valid JSON but has closing brace }''' + + extracted = _extract_json_from_response(test_response_invalid_json) + assert extracted == "{ this is not valid JSON but has closing brace }" + + # But it should fail when trying to parse as JSON + with pytest.raises(json.JSONDecodeError): + json.loads(extracted) + + def test_multiple_json_objects(self): + """Test that it extracts from first { to last } when multiple objects exist.""" + test_response = '''First object: {"a": 1} and second object: {"b": 2}''' + + extracted = _extract_json_from_response(test_response) + + # Should extract from first { to last } + assert extracted == '''{"a": 1} and second object: {"b": 2}''' + + def test_with_pydantic_models(self): + """Test that extracted JSON works with Pydantic model validation.""" + # Test ResearchValidationResponse + research_response = '''```json +{ + "research_adequate": true, + "design_based_on_research": false, + "issues": ["Design not based on research"], + "feedback": "Research is good but design needs alignment" +} +```''' + + extracted = _extract_json_from_response(research_response) + model = ResearchValidationResponse.model_validate_json(extracted) + + assert model.research_adequate is True + assert model.design_based_on_research is False + assert "Design not based on research" in model.issues + assert "alignment" in model.feedback + + # Test JudgeResponse + judge_response = '''```json +{ + "approved": false, + "required_improvements": ["Add error handling", "Improve documentation"], + "feedback": "Code needs improvements before approval" +} +```''' + + extracted = _extract_json_from_response(judge_response) + model = JudgeResponse.model_validate_json(extracted) + + assert model.approved is False + assert len(model.required_improvements) == 2 + assert "error handling" in model.required_improvements[0] + + # Test WorkflowGuidance + workflow_response = '''```json +{ + "next_tool": "judge_code_change", + "reasoning": "Code has been written and needs review", + "preparation_needed": ["Gather code changes", "Document requirements"], + "guidance": "Call judge_code_change with the written code" +} +```''' + + extracted = _extract_json_from_response(workflow_response) + model = WorkflowGuidance.model_validate_json(extracted) + + assert model.next_tool == "judge_code_change" + assert "review" in model.reasoning + assert len(model.preparation_needed) == 2 diff --git a/tests/test_models.py b/tests/test_models.py index f8503a0..31cd2f1 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -1,59 +1,60 @@ #!/usr/bin/env python3 -""" -Test the response models for MCP as a Judge. -""" +"""Test the response models for MCP as a Judge.""" -import sys import os -sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'src')) +import sys from mcp_as_a_judge.models import JudgeResponse +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src")) + -def test_judge_response_model(): +def test_judge_response_model() -> None: """Test that the JudgeResponse model works correctly.""" print("Testing JudgeResponse model...") - + # Test approved response approved_response = JudgeResponse( approved=True, required_improvements=[], - feedback="The coding plan follows all best practices." + feedback="The coding plan follows all best practices.", ) - - assert approved_response.approved == True + + assert approved_response.approved assert approved_response.required_improvements == [] assert "best practices" in approved_response.feedback print("โœ“ Approved response model works") - + # Test needs revision response revision_response = JudgeResponse( approved=False, required_improvements=[ "Add input validation", "Implement proper error handling", - "Add unit tests" + "Add unit tests", ], - feedback="The code needs several improvements before approval." + feedback="The code needs several improvements before approval.", ) - - assert revision_response.approved == False + + assert not revision_response.approved assert len(revision_response.required_improvements) == 3 assert "Add input validation" in revision_response.required_improvements print("โœ“ Needs revision response model works") - + # Test JSON serialization json_data = approved_response.model_dump() - assert json_data["approved"] == True + assert json_data["approved"] assert json_data["required_improvements"] == [] print("โœ“ JSON serialization works") - + # Test JSON deserialization reconstructed = JudgeResponse(**json_data) assert reconstructed.approved == approved_response.approved - assert reconstructed.required_improvements == approved_response.required_improvements + assert ( + reconstructed.required_improvements == approved_response.required_improvements + ) print("โœ“ JSON deserialization works") - + print("โœ“ All model tests passed!") assert True # All checks passed diff --git a/tests/test_prompt_loader.py b/tests/test_prompt_loader.py new file mode 100644 index 0000000..068d0a0 --- /dev/null +++ b/tests/test_prompt_loader.py @@ -0,0 +1,146 @@ +"""Tests for the prompt loader functionality.""" + +from pathlib import Path + +import pytest + +from mcp_as_a_judge.prompt_loader import PromptLoader, prompt_loader + + +class TestPromptLoader: + """Test the PromptLoader class.""" + + def test_prompt_loader_initialization(self) -> None: + """Test that prompt loader initializes correctly.""" + loader = PromptLoader() + assert loader.prompts_dir.exists() + assert loader.prompts_dir.name == "prompts" + + def test_custom_prompts_dir(self) -> None: + """Test initialization with custom prompts directory.""" + custom_dir = Path(__file__).parent / "fixtures" + loader = PromptLoader(custom_dir) + assert loader.prompts_dir == custom_dir + + def test_load_template_success(self) -> None: + """Test loading an existing template.""" + template = prompt_loader.load_template("user/judge_coding_plan.md") + assert template is not None + assert hasattr(template, "render") + + def test_load_template_not_found(self) -> None: + """Test loading a non-existent template raises error.""" + with pytest.raises( + FileNotFoundError, match="Template 'nonexistent.md' not found" + ): + prompt_loader.load_template("nonexistent.md") + + def test_render_judge_coding_plan_user(self) -> None: + """Test rendering the judge coding plan user prompt.""" + prompt = prompt_loader.render_prompt( + "user/judge_coding_plan.md", + user_requirements="Build a calculator", + plan="Create Python calculator", + design="Use functions for operations", + research="Researched Python math", + context="Educational project", + ) + + assert "Build a calculator" in prompt + assert "Create Python calculator" in prompt + assert "Use functions for operations" in prompt + assert "Researched Python math" in prompt + assert "Educational project" in prompt + assert "Please evaluate the following coding plan" in prompt + + def test_render_judge_coding_plan_system(self) -> None: + """Test rendering the judge coding plan system prompt.""" + prompt = prompt_loader.render_prompt( + "system/judge_coding_plan.md", + response_schema='{"type": "object"}', + ) + + assert '{"type": "object"}' in prompt + assert "Software Engineering Judge" in prompt + assert "expert software engineering judge" in prompt + + def test_render_judge_code_change_user(self) -> None: + """Test rendering the judge code change user prompt.""" + prompt = prompt_loader.render_prompt( + "user/judge_code_change.md", + user_requirements="Fix the bug", + code_change="def add(a, b): return a + b", + file_path="calculator.py", + change_description="Added addition function", + ) + + assert "Fix the bug" in prompt + assert "def add(a, b): return a + b" in prompt + assert "calculator.py" in prompt + assert "Added addition function" in prompt + assert "Please review the following code" in prompt + + def test_render_research_validation_user(self) -> None: + """Test rendering the research validation user prompt.""" + prompt = prompt_loader.render_prompt( + "user/research_validation.md", + user_requirements="Build a web app", + plan="Use Flask framework", + design="MVC architecture", + research="Compared Flask vs Django", + ) + + assert "Build a web app" in prompt + assert "Use Flask framework" in prompt + assert "MVC architecture" in prompt + assert "Compared Flask vs Django" in prompt + assert "Please validate the research quality" in prompt + + def test_render_prompt_generic(self) -> None: + """Test the generic render_prompt method.""" + prompt = prompt_loader.render_prompt( + "user/judge_coding_plan.md", + user_requirements="Test requirement", + plan="Test plan", + design="Test design", + research="Test research", + context="Test context", + ) + + assert "Test requirement" in prompt + assert "Test plan" in prompt + assert "Test design" in prompt + assert "Test research" in prompt + assert "Test context" in prompt + + def test_jinja_template_features(self) -> None: + """Test that Jinja2 features work correctly.""" + # Test with empty context + prompt = prompt_loader.render_prompt( + "user/judge_coding_plan.md", + user_requirements="Test", + plan="Test", + design="Test", + research="Test", + context="", # Empty context + ) + + # Should not have broken formatting and should contain all test values + assert "## Context" in prompt + assert "## Plan" in prompt + assert prompt.count("Test") >= 4 # Should appear at least 4 times (our inputs) + + def test_global_prompt_loader_instance(self) -> None: + """Test that the global prompt_loader instance works.""" + assert prompt_loader is not None + assert isinstance(prompt_loader, PromptLoader) + + # Should be able to render prompts + prompt = prompt_loader.render_prompt( + "user/judge_coding_plan.md", + user_requirements="Global test", + plan="Global plan", + design="Global design", + research="Global research", + ) + assert "Global test" in prompt diff --git a/tests/test_server.py b/tests/test_server.py index 4d0b82b..bc8a059 100644 --- a/tests/test_server.py +++ b/tests/test_server.py @@ -1,31 +1,29 @@ -""" -Test the MCP server functionality. -""" +"""Test the MCP server functionality.""" -import pytest from mcp_as_a_judge import mcp -def test_server_initialization(): +def test_server_initialization() -> None: """Test that the server can be initialized and tools are registered.""" # Check that the server instance exists - assert mcp.name == "MCP as a Judge" + assert mcp.name == "MCP-as-a-Judge" # Check that the server has the expected attributes - assert hasattr(mcp, 'name') + assert hasattr(mcp, "name") -def test_server_tools_registered(): +def test_server_tools_registered() -> None: """Test that the expected tools are registered.""" # The tools are registered via decorators, so they should be available # when the server runs. We can't easily inspect them here, but we can # verify the server instance exists and has the expected structure. assert mcp is not None - assert mcp.name == "MCP as a Judge" + assert mcp.name == "MCP-as-a-Judge" -def test_server_import(): +def test_server_import() -> None: """Test that the server can be imported without errors.""" from mcp_as_a_judge import mcp as imported_mcp + assert imported_mcp is not None - assert imported_mcp.name == "MCP as a Judge" + assert imported_mcp.name == "MCP-as-a-Judge" diff --git a/tests/test_server_startup.py b/tests/test_server_startup.py index 14cfd0d..33657d5 100644 --- a/tests/test_server_startup.py +++ b/tests/test_server_startup.py @@ -1,33 +1,38 @@ #!/usr/bin/env python3 -""" -Test that the MCP server can start up properly. +"""Test that the MCP server can start up properly. + This test verifies the server initialization without running it indefinitely. """ import asyncio -import sys import os -sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'src')) +import sys + from mcp_as_a_judge.server import mcp +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src")) + -async def test_server_startup(): +async def test_server_startup() -> None: """Test that the server can be initialized properly.""" print("Testing MCP server startup...") - + try: # Test that the server has the expected name - assert mcp.name == "MCP as a Judge", f"Expected 'MCP as a Judge', got '{mcp.name}'" + assert ( + mcp.name == "MCP as a Judge" + ), f"Expected 'MCP as a Judge', got '{mcp.name}'" print(f"โœ“ Server name is correct: {mcp.name}") - + # Test that the server is a FastMCP instance from mcp.server.fastmcp import FastMCP + assert isinstance(mcp, FastMCP), f"Expected FastMCP instance, got {type(mcp)}" - print(f"โœ“ Server is FastMCP instance") - + print("โœ“ Server is FastMCP instance") + print("โœ“ All startup tests passed!") return 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