flowchart TD
    A[User Request] --> B[LLM Tool-Calling Engine]
    B --> C{Tool Category}
    
    C -- Read Tool --> D[Execute GitHub API Call Directly]
    D --> E[Return API Payload]
    
    C -- Write / Destructive --> F{confirm == true ?}
    F -- Yes --> G[Execute State Mutation on GitHub]
    G --> H[Return Success / Number / URL]
    
    F -- No --> I[Return Dry-Run Preview of Mutation]

├── app/
│   ├── __init__.py          # Public MCP server & client exports
│   ├── server.py            # FastMCP server definition & tool routing
│   └── github_client.py     # GitHub REST API client with dry-run previews
├── eval/
│   ├── __init__.py          # Evaluation package
│   ├── tool_schemas.py      # OpenAI-compatible function definitions & prompt
│   ├── test_set.json        # 30 labeled evaluation test cases
│   └── run_eval.py          # Benchmark harness scoring tool, args, and confirm flags
├── requirements.txt         # Dependencies (mcp, requests, openai)
├── WRITEUP.md               # Detailed evaluation report & iteration analysis
└── README.md

git clone https://github.com/vishalmurugan1986/github-mcp-server.git
cd github-mcp-server

pip install -r requirements.txt

export NVIDIA_API_KEY="nvapi-your-key-here"
# Optional: real GitHub write access
export GITHUB_TOKEN="ghp_your_personal_access_token"

python -m app.server

python -m eval.run_eval