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Lucidity MCP - Implementation Plan

This checklist outlines the steps to build and deploy Lucidity MCP using Python and the FastMCP SDK.

Phase 1: Setup and Environment ✅

  • Create GitHub repository (lucidity-mcp)
  • Set up Python development environment
  • Install core dependencies:
    • FastMCP SDK
    • Testing frameworks (pytest)
    • Documentation tools
  • Set up project structure following Python best practices
  • Create initial README with project description and setup instructions
  • Set up GitHub Actions for CI/CD

Phase 2: Core Implementation ✅

  • Define server configuration and metadata
    • Server name, version, description
    • Capability declarations
  • Implement the core MCP server using FastMCP
    • Setup basic server skeleton
    • Configure stdio transport
    • Implement initialization logic

Phase 3: Issue Definitions and Prompts ✅

  • Define the comprehensive catalog of code quality issues:
    • Unnecessary complexity
    • Poor abstractions
    • Unintended code deletion
    • Hallucinated components
    • Style inconsistencies
    • Security vulnerabilities
    • Performance issues
    • Code duplication
    • Incomplete error handling
    • Test coverage gaps
  • For each issue type, define:
    • Clear name and description
    • Detailed checkpoints for analysis
    • Severity classification guidelines
  • Implement prompt generation logic
    • Base prompt template with instructions and response format
    • Language-specific adaptations
    • Original vs. new code comparison handling
    • Issue-specific prompt sections

Phase 4: Tool Implementation ✅

  • Implement the analyze_changes tool
    • Define input schema (code, original code, language, focus areas)
    • Implement tool execution handler
    • Generate structured analysis prompts
    • Format and return results

Phase 5: Testing 🔄

  • Implement unit tests for all components
    • Core server functionality
    • Prompt generation logic
    • Tool implementation
  • Create integration tests with mock MCP clients
  • Develop a suite of example code samples for testing
    • Samples demonstrating each issue type
    • Multi-issue examples
    • Different programming languages
  • Manual testing with Claude for Desktop
  • Collect and analyze test results
  • Refine implementation based on test findings

Phase 6: Documentation 🔄

  • Complete API documentation
  • Create usage examples for different scenarios
  • Document installation and setup process
  • Create troubleshooting guide
  • Implement inline code documentation
  • Develop user guide with:
    • Setup instructions
    • Integration with different MCP clients
    • Example usage patterns
    • Customization options

Phase 7: Refinement

  • Optimize prompt generation
  • Refine issue definitions based on testing
  • Implement feedback mechanism for issue detection quality
  • Add support for additional languages or language-specific checks
  • Optimize performance for large codebases
  • Implement caching if needed

Phase 8: Deployment and Distribution

  • Package for PyPI distribution
  • Create deployment documentation
  • Set up versioning strategy
  • Create release notes for initial version
  • Publish to PyPI
  • Set up update mechanism

Phase 9: Integration Examples

  • Create integration examples with:
    • Claude for Desktop
    • VS Code via custom MCP client
    • CI/CD pipelines
  • Document integration patterns

Phase 10: Community and Support

  • Set up issue templates on GitHub
  • Create contribution guidelines
  • Establish support channels
  • Develop plan for ongoing maintenance
  • Create community engagement strategy

New Phase: SSE Transport Enhancement ✅

  • Implement SSE (Server-Sent Events) transport
    • Create HTTP server for network-based MCP connections
    • Configure CORS for API access
    • Implement proper shutdown and error handling
  • Enhance logging system
    • Support multiple logging modes (console, file, stderr)
    • Add proper error handling and exception tracking
    • Configure log levels appropriately for different components

Future Enhancements (Post-MVP)

  • Add customization options for prompts
  • Implement persistent storage for analysis history
  • Create visualization for code quality trends
  • Develop language-specific analysis enhancements
  • Implement project-level analysis capabilities
  • Add multi-file analysis support
  • Create plugin system for custom issue types