legacy_client_mcp_server

GitHub Repository Assistant

A tool that uses the MCP (Model Control Protocol) to connect Claude with GitHub repositories, allowing users to analyze code and get assistance with tasks like creating deployment workflows.

Features

• Clone and analyze any GitHub repository
• Interact with repositories through Claude 3.5 Sonnet
• Request deployment workflows and other code generation tasks
• Seamless integration via Streamlit frontend
• MCP server with specialized tools for repository interaction

Requirements

• Python 3.13 or higher
• Git installed and available in PATH
• An Anthropic API key

Installation

1. Clone this repository:

   git clone <your-repo-url>
   cd <repo-directory>
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

3. Set your Anthropic API key in the .env file:

   ANTHROPIC_API_KEY=your_api_key_here
   

Usage

1. Start the Streamlit application:

   streamlit run app.py
   

2. In the sidebar, connect to the MCP server by entering the path to the server.py file in the "Server Script Path" field and clicking "Connect to Server".

3. Enter a GitHub repository URL (e.g., https://github.com/deploypilotorg/example-repo) and click "Analyze Repository".

4. Once the repository is analyzed, you can interact with it by asking questions or requesting tasks like:

   • "What files are in this repository?"
   • "What does the main.py file do?"
   • "Create a GitHub Actions workflow for deploying this code"
   • "Generate a Dockerfile for this application"

How It Works

The application uses the Model Control Protocol (MCP) to enable Claude to interact with GitHub repositories through specialized tools:

1. The MCP server (server.py) provides tools for cloning, analyzing, and reading from GitHub repositories
2. The Streamlit frontend (app.py) provides a user-friendly interface for interactions
3. Claude uses the provided tools to understand repository structure and generate relevant responses

Limitations

• Currently only supports public GitHub repositories
• Large repositories may take longer to clone and analyze
• Generated deployment workflows may need manual adjustments based on specific requirements