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.

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
Pull a recommended model:
ollama pull llama3:8b
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:

Server Layer: Handles MCP protocol communication and routes requests
Expert Layer: Encapsulates code review logic for each expert
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

Integrations