ai-coding-gym-mcp 0.1.7

MCP server for AI Coding Gym - fetch and submit coding challenges

AI Coding Gym - MCP Server

Local MCP server for interacting with the AI Coding Gym platform. Provides tools to fetch coding problems and submit solutions.

Features

• /fetch: Fetch a coding problem and clone the repository to your local machine
• /submit: Submit your solution by committing and pushing changes

Quick Start

Installation

Option 1: Install from PyPI

pip install ai-coding-gym-mcp

Option 2: Install from GitHub

pip install git+https://github.com/yourusername/ai-coding-gym-mcp.git

Option 3: Install from source

git clone https://github.com/yourusername/ai-coding-gym-mcp.git
cd ai-coding-gym-mcp
pip install -e .

Configure Claude Desktop

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on Mac):

{
  "mcpServers": {
    "ai-coding-gym": {
      "command": "ai-coding-gym-mcp"
    }
  }
}

Configure VS Code (Copilot with MCP)

Add to your VS Code settings (.vscode/settings.json or User Settings):

{
  "github.copilot.chat.codeGeneration.instructions": [
    {
      "text": "Use AI Coding Gym MCP tools for problem solving"
    }
  ],
  "mcp.servers": {
    "ai-coding-gym": {
      "command": "python",
      "args": ["-m", "server"],
      "cwd": "/path/to/ai-coding-gym-mcp"
    }
  }
}

Or if installed via pip, use the executable directly:

{
  "mcp.servers": {
    "ai-coding-gym": {
      "command": "ai-coding-gym-mcp"
    }
  }
}

Usage

Running the MCP Server

The server uses stdio for communication with MCP clients:

python server.py

Or configure it in your MCP client settings (e.g., Claude Desktop).

Tool: /configure

Configure the MCP server with your user ID. This generates an SSH key pair and registers it with the server.

Parameters:

• user_id (required): Your user ID for authentication
• workspace_dir (optional): Default workspace directory (default: "./workspace")

Example:

{
  "user_id": "user_123",
  "workspace_dir": "./workspace"
}

What it does:

1. Generates an SSH key pair locally (stored in ~/.mcp-keys/)
2. Sends the public key to the server
3. Receives your repository name
4. Stores configuration for future use

Tool: /fetch

Fetches a problem from the backend and clones the repository locally.

Parameters:

• problem_id (required): Problem identifier (e.g., "django__django-10097")
• user_id (optional): Your user ID (uses configured value if not provided)
• workspace_dir (optional): Local workspace directory (default: "./workspace")

Example:

{
  "problem_id": "django__django-10097"
}

What it does:

1. Uses your SSH key from /configure to access the repository
2. Clones only the specific problem branch (shallow clone)
3. Sets up the workspace at workspace/{problem_id}/

Tool: /submit

Submits your solution by committing changes and pushing to the remote repository.

Parameters:

• problem_id (required): Problem identifier
• user_id (optional): Your user ID (uses configured value if not provided)
• commit_message (optional): Custom commit message

Example:

{
  "problem_id": "django__django-10097",
  "commit_message": "Fixed the authentication bug"
}

What it does:

1. Stages all changes in the working directory (git add -A)
2. Commits with the provided or auto-generated message
3. Pushes to the remote branch using deployment key
4. Notifies the backend server about the submission

Backend API Endpoints

The MCP server connects to the hardcoded AI Coding Gym server and uses the following endpoints:

POST /api/configure

Request:

{
  "user_id": "user_123",
  "public_key": "ssh-rsa AAAAB3..."
}

Response:

{
  "repo_name": "user_123-swebench"
}

POST /api/submit

Request:

{
  "problem_id": "django__django-10097",
  "user_id": "user_123",
  "commit_hash": "abc123def456...",
  "branch": "django__django-10097-user_123",
  "timestamp": "2026-02-03T10:30:00"
}

Response:

{
  "status": "success",
  "message": "Submission received"
}

Security

• User SSH keys are stored in ~/.mcp-keys/ with 600 permissions
• Keys are generated locally and public key is shared with the server
• SSH host key checking is disabled for convenience (consider enabling in production)
• Configuration is cached in memory during the MCP server session

Troubleshooting

"No credentials found for problem_id"

• Run /configure first to set up your credentials
• Then run /fetch to download the problem

"Git clone/push failed"

• Check network connectivity
• Verify deployment key is valid
• Ensure SSH agent isn't interfering

"Directory already exists"

• Remove the existing directory or use a different workspace location

Development

The server uses:

• mcp: Model Context Protocol SDK
• requests: HTTP client for backend API calls
• subprocess: Git command execution with SSH key management

Local Development

# Clone the repository
git clone https://github.com/yourusername/ai-coding-gym-mcp.git
cd ai-coding-gym-mcp

# Install in development mode
pip install -e .

# Run tests (if available)
pytest

# Test the server locally
python server.py

Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

License

MIT