Code Review MCP Server

# LLM Integration Implementation Plan ## Overview This document outlines the step-by-step implementation plan for integrating Large Language Model (LLM) capabilities into the code-review-server. The integration will take the output of Repomix (flattened codebase or selected files) and send it to an LLM with a code review prompt to get a comprehensive code review with structured output. ## Goals 1. Implement a flexible LLM integration that supports multiple providers (OpenAI, Anthropic, etc.) 2. Process Repomix output and prepare it for LLM analysis 3. Design structured output format for code reviews 4. Handle authentication via environment variables for API keys 5. Implement error handling and retry mechanisms 6. Create a user-friendly interface for configuring and running code reviews ## Architecture ``` ┌───────────┐ ┌───────────┐ ┌───────────┐ ┌────────────┐ │ │ │ │ │ │ │ │ │ Repomix │────▶│ Formatter │────▶│ LLM Agent │────▶│ Structured │ │ │ │ │ │ │ │ Output │ └───────────┘ └───────────┘ └───────────┘ └────────────┘ ``` ## Implementation Checklist ### Phase 1: Initial Setup and Core Functionality - [x] Set up project structure for LLM integration - [x] Create LLM provider abstraction layer - [x] Implement environment variable configuration - [x] Create basic prompt templates for code review - [x] Implement JSON parsing for structured output - [x] Add basic error handling and logging ### Phase 2: Code Processing and Integration - [x] Implement Repomix output processing - [x] Create chunking logic for large codebases - [x] Build prompt construction with code content - [x] Integrate with LLM providers through Mastra - [x] Implement retry logic for API calls - [x] Create end-to-end flow from Repomix to LLM review ### Phase 3: MCP Tool Implementation - [x] Register the code_review MCP tool - [x] Implement the tool handler with proper parameters - [x] Add parameter validation and error handling - [ ] Test tool with simple repositories - [ ] Update tool to handle large codebases effectively ### Phase 4: Testing and Refinement - [ ] Create unit tests for components - [ ] Add integration tests for end-to-end flow - [ ] Test with various codebases and repositories - [ ] Optimize prompt for better results - [ ] Refine structured output format as needed - [ ] Performance testing and optimization ### Phase 5: Documentation and Polish - [x] Document API and configuration options - [x] Create examples and usage guides - [x] Update README with setup instructions - [x] Add security guidelines for API keys - [ ] Implement additional features based on feedback ## Timeline - Phase 1: 2 days - Phase 2: 3 days - Phase 3: 2 days - Phase 4: 2 days - Phase 5: 1 day Total: 10 working days ## Dependencies - Mastra SDK for LLM integration - @modelcontextprotocol/sdk for MCP server - Repomix for code flattening - Node.js fs/path modules for file operations - Environment variable management - JSON parsing and validation ## Security Considerations - API keys must be stored securely as environment variables - Sensitive code content should not be logged - Rate limiting should be implemented to avoid excessive API costs - Temporary files should be properly managed and cleaned up - Input validation to prevent injection attacks ## Conclusion This implementation plan provides a comprehensive guide for integrating LLM capabilities into the code-review-server. By following this plan, we can create a robust, flexible system that leverages LLMs to provide valuable code reviews with structured outputs.