GitHub Code Explorer using Model Context Protocol
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An example to demonstrate how to build and run an MCP server
Details
- Author
- anupj
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Jump to
- Search for code across GitHub repositories
- View file contents from repositories
- Search for GitHub repositories
- Function discovery endpoint (GET /api/mcp/functions)
- AI assistant simulation interface
Clone the repository, install dependencies with npm install, optionally set the GH_TOKEN environment variable for higher rate limits, then start the server with npm start (or npm run dev for auto-reload). Open http://localhost:3000 in a browser, click “Load Available Functions” to see the API capabilities, and type a query to simulate an AI conversation.
GitHub Code Explorer using Model Context Protocol
This project demonstrates how to implement the Model Context Protocol (MCP) to create a GitHub code search tool that could be used by AI assistants.
What is Model Context Protocol?
Model Context Protocol is a standardized way for AI models to interact with external tools and services. It defines:
1. How functions are described to AI models
2. How AI models decide which functions to call
3. How parameters are structured and validated
4. How results are returned in a consistent format
Features
- Search for code across GitHub repositories
- View file contents from repositories
- Search for GitHub repositories
- Function discovery endpoint
- AI assistant simulation interface
Installation
1. Clone this repository
2. Install dependencies:
npm install
3. Optional: Set up a GitHub API token as the environment variable
GH_TOKEN for higher rate limits
Usage
1. Start the server:
npm start
or for development with auto-reload:
npm run dev
2. Open your browser and navigate to http://localhost:3000
3. Click "Load Available Functions" to see the API capabilities
4. Type a query in the input box and press Enter to simulate an AI conversation
API Endpoints
- GET /api/mcp/functions - List all available functions
- POST /api/mcp - Call a function with parameters
Implementation Details
The application consists of:
- A Node.js/Express backend that wraps the GitHub API
- A browser client that simulates an AI assistant using the API
- MCP-compatible function definitions with JSON Schema
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