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 is required for AI-powered code reviews.

1. Install Ollama for your platform:

   • macOS: Download from ollama.com
   • Linux: curl -fsSL https://ollama.com/install.sh | sh
   • Windows: Windows WSL2 support via Linux instructions

2. Pull a recommended model:

   ollama pull llama3:8b
   

3. Start the Ollama server:

   ollama serve
   

Installation

Run the setup script to install dependencies and create the virtual environment:

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)

source .venv/bin/activate
python server.py

HTTP/SSE Mode (for Web Integration)

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:

python server.py --transport sse --port 9000

Installing in Cursor

To install in Cursor IDE:

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:

{
  "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