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MCP Prompt Optimizer

MCP tool to analyze and optimize prompts from the prompt_engineering repository, improving accuracy and token usage.

Quick Start

Installation

Option 1: Automated (Recommended)

# Linux/macOS
./install.sh

# Windows (PowerShell)
.\install.ps1

Option 2: Manual

# Install UV if not already installed
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install dependencies
uv sync

IDE Configuration

The tool is ready to use in Cursor, VS Code, and Windsurf. Configuration files are created automatically by the install script with absolute paths for reliability.

For detailed setup instructions, see SETUP.md

Quick Configuration Options

  • Project-level (default): Configuration files are created in .cursor/, .vscode/, or .windsurf/ directories
  • Global Cursor config: Run ./install.sh --global to also configure ~/.cursor/mcp.json for system-wide use

Verify Installation

uv run python scripts/verify.py

Features

  • Prompt Analysis: Token counting, best practices validation, structure analysis
  • Optimization: Intelligent compression while maintaining quality
  • Cross-Platform Validation: Consistency across platforms for the same role
  • Test Generation: Automatic JSON test creation from prompt.toon.md

Project Structure

mcp-prompt-optimizer/
├── src/
│   ├── analyzers/      # Analyzers (token, prompt, consistency)
│   ├── optimizers/     # Optimizers (token, structure)
│   ├── validators/     # Validators (toon, test generator)
│   ├── utils.py        # Utilities for repository integration
│   └── mcp_server.py  # Main MCP server
├── config/             # Configurations and best practices
├── scripts/            # Utility scripts (verify, etc.)
└── tests/             # Unit tests

Available MCP Tools

analyze_prompt

Analyze a prompt.toon.md:

  • Token usage per block
  • Best practices score (0-100)
  • Toon structure validation
  • Recommendations

optimize_prompt

Optimize a prompt:

  • Token reduction (target: 15-30%)
  • Structure improvement
  • Best practices application
  • Generate optimized version

validate_consistency

Validate cross-platform consistency:

  • Identify core vs specific competencies
  • Calculate consistency score
  • Detect gaps between platforms
  • Suggest normalizations

token_analysis

Detailed token analysis:

  • Breakdown per block
  • Cost estimate per execution
  • Comparison with platform limits

generate_tests

Automatically generate JSON tests:

  • Baseline/edge-case/compliance scenarios
  • Assertions based on output schema
  • Repository-compliant templates

compare_prompts

Compare two prompts:

  • Token usage difference
  • Efficiency analysis
  • Improvement identification

import_readme

Import and analyze README from prompt_engineering repository:

  • Extract platform information
  • Extract best practices
  • Extract toon format info
  • Get repository structure

compare_json_vs_toon

Compare JSON vs TOON representation for a prompt:

  • Show token savings using TOON format
  • Compare file sizes
  • Estimate format efficiency

Usage

Via MCP Server (Recommended)

The tool is exposed as an MCP server. Once configured in your IDE, you can use all the tools directly.

Start the server manually (for testing):

uv run python -m src.mcp_server
# or
uv run mcp-prompt-optimizer

Via Python API

See EXAMPLE.md for examples of direct Python class usage.

IDE Configuration

  • Cursor: See SETUP.md for Cursor configuration
  • VS Code: See SETUP.md for VS Code configuration
  • Windsurf: See SETUP.md for Windsurf configuration

Import README

To import the README from the prompt_engineering repository:

from src.analyzers.readme_importer import ReadmeImporter

importer = ReadmeImporter()
context = importer.import_to_context()

# Access:
# - context['readme_content']: full content
# - context['platforms']: list of platforms with roles
# - context['best_practices']: extracted best practices
# - context['toon_format']: toon format info
# - context['repository_structure']: repository structure

Installation Methods

Local Installation (Recommended)

Install in the project directory:

uv sync

This creates a virtual environment and installs dependencies locally.

Global Installation

Install as a global package:

uv pip install -e .

Then use from anywhere:

mcp-prompt-optimizer

Development Installation

For development with dev dependencies:

uv sync --dev

Supported Best Practices

  • Context stacking
  • Explicit role + responsibilities
  • Verifiable formats (JSON/YAML/Markdown)
  • Positive/negative examples
  • Citation management
  • Modular toon format

TOON Format Integration

The tool uses toon-format for:

  • Optimized parsing of .toon.md files
  • JSON vs TOON efficiency comparison (30-60% token reduction)
  • Support for native TOON format in addition to YAML frontmatter

All parsers use ToonParser which supports both YAML frontmatter (standard) and pure TOON format.

Success Metrics

  • Accuracy: Best practices score >80%
  • Token Usage: 15-30% reduction while maintaining quality
  • Consistency: >90% alignment across platforms for the same role
  • Validation: Automatic test generation with >95% coverage

Configuration

Environment Variables

  • MCP_PROMPT_OPTIMIZER_REPO_PATH: Path to the prompt_engineering repository
    • Default: ../prompt_engineering (relative to mcp-prompt-optimizer)
    • Can be set in IDE configuration files or system environment

IDE Configuration Files

Configuration files are automatically created by the install script:

  • .cursor/mcp.json - Cursor configuration
  • .vscode/settings.json - VS Code configuration
  • .windsurf/mcp.json - Windsurf configuration

Example files are available in config/ directory.

Troubleshooting

See SETUP.md for detailed troubleshooting guide.

Common issues:

  • MCP server not starting → Check UV installation and dependencies
  • Tools not available → Verify IDE configuration and restart IDE
  • Repository path issues → Set MCP_PROMPT_OPTIMIZER_REPO_PATH environment variable