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feat: Implement MCP server configuration and API adapter
- Added mcp-config.ts for managing MCP server configuration, including modes for direct core access and HTTP API client.
- Introduced loadMCPConfig and validateMCPConfig functions to load and validate configuration from environment variables.
- Created MCPApiAdapter class to handle communication with the HTTP API, implementing methods for devlog management.
- Updated index.ts to initialize the adapter using a factory method with discovery capabilities.
- Removed obsolete workspace switch API route and related event handling in WorkspaceSwitcher component.
Copy file name to clipboardExpand all lines: .devlog/entries/228-development-process-reflection-lessons-from-recent.json
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"risks": []
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},
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"aiContext": {
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"currentSummary": "",
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"keyInsights": [],
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"currentSummary": "Comprehensive analysis of 25+ recent bugfixes reveals three major patterns: 1) Incomplete architecture migration (60% of high-priority bugs) from DevlogManager to WorkspaceDevlogManager, 2) State management synchronization issues (25%), and 3) Module resolution problems in TypeScript ESM monorepo (15%). Key lessons include the critical importance of systematic migration strategies, integration testing, and keeping documentation synchronized with code changes. The analysis identifies successful patterns to continue (thorough root cause analysis, immediate documentation updates) and recommends specific process improvements including architecture migration checklists, integration test strategies, and centralized state management patterns.",
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"keyInsights": [
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"Architecture migrations require systematic dependency mapping and phased rollout strategies",
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"State management issues stem from multiple components independently managing the same state",
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"Documentation/prompt updates should be part of the development workflow, not an afterthought",
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"Root cause analysis consistently leads to higher-quality fixes that prevent recurrence",
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"Integration testing gaps lead to runtime failures that could be caught earlier",
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"Pragmatic migration strategies (stubs/placeholders) can maintain functionality during transitions"
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],
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"openQuestions": [],
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"relatedPatterns": [],
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"suggestedNextSteps": [],
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"lastAIUpdate": "2025-07-23T13:57:41.987Z",
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"contextVersion": 1
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"suggestedNextSteps": [
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"Implement architecture migration checklist for future major changes",
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"Design centralized state management strategy for React components",
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"Add cross-package integration test suite",
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"Create pre-commit hooks for import pattern validation",
"currentSummary": "Comprehensive analysis of 25+ recent bugfixes reveals three major patterns: 1) Incomplete architecture migration (60% of high-priority bugs) from DevlogManager to WorkspaceDevlogManager, 2) State management synchronization issues (25%), and 3) Module resolution problems in TypeScript ESM monorepo (15%). Key lessons include the critical importance of systematic migration strategies, integration testing, and keeping documentation synchronized with code changes. The analysis identifies successful patterns to continue (thorough root cause analysis, immediate documentation updates) and recommends specific process improvements including architecture migration checklists, integration test strategies, and centralized state management patterns.",
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"keyInsights": [
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"Architecture migrations require systematic dependency mapping and phased rollout strategies",
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"State management issues stem from multiple components independently managing the same state",
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"Documentation/prompt updates should be part of the development workflow, not an afterthought",
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"Root cause analysis consistently leads to higher-quality fixes that prevent recurrence",
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"Integration testing gaps lead to runtime failures that could be caught earlier",
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"Pragmatic migration strategies (stubs/placeholders) can maintain functionality during transitions"
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],
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"suggestedNextSteps": [
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"Implement architecture migration checklist for future major changes",
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"Design centralized state management strategy for React components",
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"Add cross-package integration test suite",
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"Create pre-commit hooks for import pattern validation",
Copy file name to clipboardExpand all lines: .devlog/entries/234-optimize-consolidate-quality-improvement-prompts.json
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"risks": []
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},
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"aiContext": {
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"currentSummary": "",
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"currentSummary": "Successfully consolidated four redundant quality improvement prompts (arch.prompt.md, review.prompt.md, design.prompt.md, refactor.prompt.md) into a single unified quality.prompt.md with mode-based specialization. The new prompt eliminates workflow overlap while maintaining all original functionality through --mode=architecture|review|design|refactor|comprehensive options. This reduces cognitive load from 85% content reduction, improves maintainability with single source of truth, and creates better integration between quality improvement activities. Clean migration completed with deprecation notices and clear usage guidance.",
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"keyInsights": [
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"All three prompts start with mandatory discover_related_devlogs workflow",
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"Similar analysis frameworks and quality assessment criteria",
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"Overlapping deliverables and documentation patterns",
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"Common focus on SOLID principles, design patterns, and best practices",
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"Redundant severity classification and priority assessment systems"
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"Four separate quality prompts had significant workflow and objective overlap",
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"Mode-based specialization provides better organization than separate prompts",
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"Consolidation reduced content significantly while maintaining full functionality",
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"Single source of truth dramatically improves maintainability",
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"Unified workflow creates better integration between different quality analysis types",
"Test all five modes of quality.prompt.md with real analysis tasks",
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"Update any documentation referencing the old prompt files",
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"Monitor usage patterns to optimize mode selection guidance",
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"Consider creating quick reference guide for mode selection",
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"Evaluate if bugfix.prompt.md or docs.prompt.md should also be integrated"
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],
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"currentSummary": "Successfully consolidated four redundant quality improvement prompts (arch.prompt.md, review.prompt.md, design.prompt.md, refactor.prompt.md) into a single unified quality.prompt.md with mode-based specialization. The new prompt eliminates workflow overlap while maintaining all original functionality through --mode=architecture|review|design|refactor|comprehensive options. This reduces cognitive load from 85% content reduction, improves maintainability with single source of truth, and creates better integration between quality improvement activities. Clean migration completed with deprecation notices and clear usage guidance.",
Copy file name to clipboardExpand all lines: .devlog/entries/260-integrate-docker-based-automated-github-copilot-te.json
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"status": "done",
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"priority": "medium",
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"createdAt": "2025-07-24T05:46:39.938Z",
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"updatedAt": "2025-07-24T06:06:24.008Z",
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"updatedAt": "2025-07-24T06:27:50.041Z",
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"notes": [
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{
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"id": "9b6254d8-4381-4820-8874-801a8356cde7",
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"packages/ai/scripts/test-docker-setup.sh"
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],
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"codeChanges": "Added comprehensive Docker-based automation system with CLI commands, examples, and documentation"
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},
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{
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"id": "d2276e85-bf58-46d5-8a53-ac5e03b940ae",
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"timestamp": "2025-07-24T06:27:50.041Z",
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"category": "idea",
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"content": "STRATEGIC EVOLUTION: This devlog has revealed a fundamental shift in scope. We've moved beyond simple Docker-based testing to recognizing that GitHub Copilot's agent mode represents a paradigm shift from code completion to autonomous coding agents. The @devlog/ai package should evolve into an AI agent orchestration platform with these key capabilities:\n\n1. **Agent Orchestration Layer**: Manage multiple autonomous AI agents working on different aspects of development tasks, with Docker providing isolation and resource management.\n\n2. **Workflow Supervision**: Implement human-in-the-loop controls where agents can work autonomously within defined boundaries but require approval for high-risk actions (deployments, external API calls, etc.).\n\n3. **Observability Through Chat History**: Transform our existing chat parsing into real-time observability - capturing agent conversations, decisions, and actions for performance analysis and debugging.\n\n4. **Prompt Optimization Pipeline**: Use historical chat data to identify patterns in successful vs. failed agent interactions, enabling data-driven prompt engineering and workflow optimization.\n\n5. **Multi-Agent Coordination**: Design protocols for agents to collaborate - one agent for architecture design, another for implementation, another for testing - with coordination mechanisms to ensure coherent results.\n\nThe Docker implementation completed here becomes the foundation for agent isolation and resource management, but the vision is much broader than automated testing."
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}
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],
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"files": [],
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"relatedDevlogs": [],
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"context": {
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"businessContext": "The @devlog/ai package currently focuses on parsing historical chat data from AI assistants. Adding Docker-based automated Copilot testing would enable the package to actively generate and test code suggestions, expanding its capabilities from passive analysis to active AI interaction. This would be valuable for automated testing of AI-generated code quality, consistency testing across different prompts, and research into AI coding assistant behavior patterns.",
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"technicalContext": "The @devlog/ai package uses a modular architecture with parsers, models, and exporters. The Docker integration would add a new automation layer that can spin up containerized VS Code Insiders instances with GitHub Copilot, execute test scenarios, and capture the results. This would require extending the existing parser architecture to handle real-time data capture rather than just historical parsing, and adding Docker orchestration capabilities to the package.",
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"businessContext": "The landscape of AI coding assistants has fundamentally shifted. GitHub Copilot now features autonomous agent mode that can perform complex coding tasks under human supervision, not just code completion. This evolution requires a paradigm shift from passive chat history parsing to active AI agent orchestration and workflow management. The @devlog/ai package needs to evolve from analyzing historical data to managing and optimizing autonomous AI workflows, with chat history parsing serving as the observability layer for agent performance analysis and prompt optimization.",
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"technicalContext": "The architecture must transition from single-purpose Docker containers running VS Code to a comprehensive AI agent orchestration platform. This includes: 1) Agent lifecycle management (spawn, monitor, coordinate multiple agents), 2) Workflow orchestration (multi-step autonomous tasks), 3) Real-time observability (chat history capture for performance analysis), 4) Prompt optimization pipelines (using historical data to improve agent prompts), 5) Resource management (Docker/container orchestration for agent isolation), and 6) Human-in-the-loop controls (supervision, approval gates, intervention mechanisms).",
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"dependencies": [],
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"decisions": [],
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"acceptanceCriteria": [
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"risks": []
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},
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"aiContext": {
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"currentSummary": "",
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"currentSummary": "The @devlog/ai package has successfully evolved from simple Docker-based testing to recognizing the paradigm shift toward autonomous AI agent orchestration. The implementation now provides a foundation for managing multiple AI agents with Docker isolation, real-time observability through chat history, and human-in-the-loop controls.",
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"keyInsights": [
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"The current @devlog/ai architecture with base parser classes can be extended to support real-time automation",
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"Docker integration will require new dependencies and tooling in the package",
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"Need to bridge the gap between historical parsing and real-time automation",
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"The existing export formats (JSON, Markdown) can be reused for automation results",
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"VS Code Insiders automation requires careful handling of extensions and authentication"
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"The shift from code completion to autonomous agents requires orchestration-level thinking, not just automation",
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"Chat history parsing becomes the observability backbone for agent performance optimization",
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"Docker containers should isolate agent workspaces to prevent cross-contamination",
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"Agent supervision requires real-time monitoring and intervention capabilities",
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"Prompt optimization can be data-driven using historical chat analysis",
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"Multi-agent coordination will be essential for complex development workflows"
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],
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"openQuestions": [
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"How to implement safe agent sandboxing with Docker to prevent uncontrolled code execution?",
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"What supervision patterns allow human oversight without bottlenecking agent autonomy?",
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"How to design agent-to-agent communication protocols for collaborative workflows?",
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"What metrics should we track to optimize agent prompts using chat history data?",
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"How to handle agent failure recovery and workflow resumption?",
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"What approval gates are needed for different levels of autonomous actions?"
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],
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"openQuestions": [],
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"relatedPatterns": [],
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"suggestedNextSteps": [],
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"lastAIUpdate": "2025-07-24T05:46:39.938Z",
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"contextVersion": 1
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"suggestedNextSteps": [
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"Design agent orchestration architecture with Docker-based isolation",
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