mcp-codebase-insight 0.1.2

MCP Codebase Insight Server

MCP Codebase Insight

MCP Codebase Insight is a server component of the Model Context Protocol (MCP) that provides intelligent analysis and insights into codebases. It uses vector search and machine learning to understand code patterns, architectural decisions, and documentation.

Features

• 🔍 Code Analysis: Analyze code for patterns, best practices, and potential improvements
• 📝 ADR Management: Track and manage Architecture Decision Records
• 📚 Documentation: Generate and manage technical documentation
• 🧠 Knowledge Base: Store and retrieve code patterns and insights using vector search
• 🐛 Debug System: Analyze and debug issues with AI assistance
• 📊 Metrics & Health: Monitor system health and performance metrics
• 💾 Caching: Efficient caching system for improved performance
• 🔒 Security: Built-in security features and best practices

Quick Start

Using as an MCP Server

1. Create an mcp.json file in your project:

{
  "mcpServers": {
    "codebase-insight": {
      "command": "mcp-codebase-insight",
      "args": [
        "--host",
        "127.0.0.1",
        "--port",
        "3000",
        "--log-level",
        "INFO"
      ],
      "env": {
        "PYTHONPATH": "${workspaceRoot}",
        "MCP_HOST": "127.0.0.1",
        "MCP_PORT": "3000",
        "MCP_LOG_LEVEL": "INFO",
        "QDRANT_URL": "http://localhost:6333",
        "MCP_DOCS_CACHE_DIR": "${workspaceRoot}/docs",
        "MCP_ADR_DIR": "${workspaceRoot}/docs/adrs",
        "MCP_KB_STORAGE_DIR": "${workspaceRoot}/knowledge",
        "MCP_DISK_CACHE_DIR": "${workspaceRoot}/cache"
      }
    }
  }
}

2. Install the package in your project:

pip install mcp-codebase-insight

3. Start the server:

mcp-codebase-insight --host 127.0.0.1 --port 8000 --log-level INFO

Using Docker

# Pull the image
docker pull modelcontextprotocol/mcp-codebase-insight

# Run the container
docker run -p 3000:3000 \
    --env-file .env \
    -v $(pwd)/docs:/app/docs \
    -v $(pwd)/knowledge:/app/knowledge \
    tosin2013/mcp-codebase-insight

Local Development Installation

1. Prerequisites:

   • Python 3.11+
   • Rust (for building dependencies)
   • Qdrant vector database

2. Clone the repository:

   git clone https://github.com/tosin2013/mcp-codebase-insight.git
   cd mcp-codebase-insight
   

3. Create a virtual environment:

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   

4. Install dependencies:

   pip install -r requirements.txt
   

5. Configure environment:

   cp .env.example .env
   # Edit .env with your settings
   

6. Run the server:

   uvicorn src.mcp_codebase_insight.server:app --reload
   

Building for Distribution

To use codebase-insight in other directories, you'll need to build and install it:

1. Create a setup.py:

from setuptools import setup, find_packages

setup(
    name="mcp-codebase-insight",
    version="0.1.0",
    packages=find_packages(where="src"),
    package_dir={"": "src"},
    install_requires=[
        "fastapi>=0.103.2",
        "uvicorn>=0.23.2",
        "pydantic>=2.4.2",
        "qdrant-client>=1.13.3",
        "sentence-transformers>=2.2.2",
        "python-dotenv>=1.0.0"
    ],
    python_requires=">=3.11",
)

2. Build the package:

pip install build
python -m build

3. Install in another project:

pip install path/to/mcp-codebase-insight/dist/mcp_codebase_insight-0.1.0.tar.gz

Configuration

Setting up mcp.json

The mcp.json file is used to configure how the MCP server runs in your development environment. Create this file in your project's root directory:

1. Create a new file named mcp.json in your project root
2. Add the following configuration, adjusting paths and settings as needed:

{
  "mcpServers": {
    "codebase-insight": {
      "command": "mcp-codebase-insight",
      "args": [
        "--host",
        "127.0.0.1",
        "--port",
        "8000",
        "--log-level",
        "INFO"
      ],
      "env": {
        "PYTHONPATH": "${workspaceRoot}",
        "MCP_HOST": "127.0.0.1",
        "MCP_PORT": "8000",
        "MCP_LOG_LEVEL": "INFO",
        "QDRANT_URL": "http://localhost:6333",
        "MCP_DOCS_CACHE_DIR": "${workspaceRoot}/docs",
        "MCP_ADR_DIR": "${workspaceRoot}/docs/adrs",
        "MCP_KB_STORAGE_DIR": "${workspaceRoot}/knowledge",
        "MCP_DISK_CACHE_DIR": "${workspaceRoot}/cache"
      }
    }
  }
}

This configuration:

• Sets up the server to run on localhost:8000
• Configures logging and debugging options
• Specifies paths for documentation, ADRs, and caching
• Sets the Qdrant vector database URL

You can customize these settings based on your needs. The server supports the following command-line options:

• --host: Host address to bind the server to (default: 127.0.0.1)
• --port: Port to run the server on (default: 3000)
• --log-level: Set the logging level (choices: DEBUG, INFO, WARNING, ERROR, CRITICAL)
• --debug: Enable debug mode

API Documentation

The API documentation is available at /docs when the server is running. Key endpoints include:

• /tools/analyze-code: Analyze code for patterns
• /tools/create-adr: Create Architecture Decision Records
• /tools/debug-issue: Debug issues with AI assistance
• /tools/search-knowledge: Search the knowledge base
• /tools/crawl-docs: Crawl documentation
• /tools/get-task: Get task status
• /health: Health check endpoint
• /metrics: Metrics endpoint

Development

Project Structure

mcp-codebase-insight/
├── docs/               # Documentation
├── src/               # Source code
│   └── mcp_codebase_insight/
│       ├── core/      # Core functionality
│       └── utils/     # Utilities
├── tests/             # Test suite
├── scripts/           # Utility scripts
└── examples/          # Example code

Development Commands

# Run tests
pytest tests -v

# Run linters
flake8 src tests

# Format code
black src tests

# Build package
python -m build

# Install locally
pip install -e .

Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Model Context Protocol
• Qdrant Vector Database
• sentence-transformers
• FastAPI

Support

• 📖 Documentation
• 🐛 Issue Tracker
• 💬 Discussions