MCP Code Expert System

# MCP Code Expert System A Python-based code review system using the Model Context Protocol (MCP). It provides code review capabilities through simulated expert personas like Martin Fowler and Robert C. Martin (Uncle Bob). ## Features - Code review based on Martin Fowler's refactoring principles - Code review based on Robert C. Martin's Clean Code principles - Knowledge graph storage of code, reviews, and relationships - Integration with Ollama for AI-powered reviews - Server-side Event (SSE) support for web integration ## Prerequisites ### Python 3.10+ This project requires Python 3.10 or higher. ### Ollama [Ollama](https://ollama.com/) is required for AI-powered code reviews. 1. Install Ollama for your platform: - **macOS**: Download from [ollama.com](https://ollama.com/) - **Linux**: `curl -fsSL https://ollama.com/install.sh | sh` - **Windows**: Windows WSL2 support via Linux instructions 2. Pull a recommended model: ```bash ollama pull llama3:8b ``` 3. Start the Ollama server: ```bash ollama serve ``` ## Installation Run the setup script to install dependencies and create the virtual environment: ```bash chmod +x setup.sh ./setup.sh ``` ## Configuration Edit the `.env` file to configure (create from `.env.example` if needed): ``` # Knowledge Graph Settings KNOWLEDGE_GRAPH_PATH=data/knowledge_graph.json # Ollama Configuration (local AI models) OLLAMA_HOST=http://localhost:11434 OLLAMA_MODEL=llama3:8b ``` ## Usage ### Running the Server #### Standard Mode (for Cursor Integration) ```bash source .venv/bin/activate python server.py ``` #### HTTP/SSE Mode (for Web Integration) ```bash source .venv/bin/activate python server.py --transport sse ``` This will start the server at `http://localhost:8000/sse` for SSE transport. For custom port: ```bash python server.py --transport sse --port 9000 ``` ### Installing in Cursor To install in Cursor IDE: ```bash source .venv/bin/activate mcp install server.py --name "Code Expert System" ``` ### Available Tools The server exposes these tools: - `ask_martin`: Ask Martin Fowler to review code and suggest refactorings - `ask_bob`: Ask Robert C. Martin (Uncle Bob) to review code based on Clean Code principles - `read_graph`: Read the entire knowledge graph - `search_nodes`: Search for nodes in the knowledge graph - `open_nodes`: Open specific nodes by their names ### Example Usage To review a code snippet with Martin Fowler: ```json { "code": "function calculateTotal(items) {\n var total = 0;\n for (var i = 0; i < items.length; i++) {\n total += items[i].price;\n }\n return total;\n}", "language": "javascript", "description": "Calculate the total price of items" } ``` ## Project Structure - `server.py`: Main server implementation with MCP integration - `experts/`: Expert modules implementing the code review capabilities - `__init__.py`: Shared models and interfaces - `martin_fowler/`: Martin Fowler expert implementation - `robert_c_martin/`: Robert C. Martin expert implementation - `knowledge_graph.py`: Knowledge graph for storing code and reviews - `ollama_service.py`: Integration with Ollama for AI-powered reviews - `examples/`: Example code for review in different languages - `requirements.txt`: Python dependencies - `setup.sh`: Setup script ## Architecture The system follows a modular architecture: 1. **Server Layer**: Handles MCP protocol communication and routes requests 2. **Expert Layer**: Encapsulates code review logic for each expert 3. **Service Layer**: Provides AI integration and knowledge graph functionality Each expert implements a standard interface allowing for consistent handling and easy addition of new experts. ## License MIT