Explore MCP

This repo runs a local MCP (Model Context Protocol) server, verifies tool connectivity with a client, and demonstrates OpenAI tool-calling integration.

Prerequisites

• Python 3.13 (per pyproject.toml).
• Linux/macOS shell (commands shown for bash).
• An OpenAI API key.
• Dependencies listed in pyproject.toml.

Install dependencies

You can use uv, poetry, or pip. Pick one approach.

Option A: uv (fast, recommended)

# If uv is not installed, see https://docs.astral.sh/uv/getting-started/
uv sync

Option B: poetry

poetry install
poetry shell

Option C: pip + venv

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt .

Set OpenAI API key

The script utils/helpers.py expects your key in a file at:

• ~/.llm_secrets with content like:

OPENAI_API_KEY=sk-...

Alternatively, you can set the environment variable directly in your shell before running the OpenAI test:

export OPENAI_API_KEY=sk-...

Note: call_my_mcp2.py imports get_api_key() and sets os.environ["OPENAI_API_KEY"] internally using the ~/.llm_secrets file. If that file is missing, set the env var as shown above or create the file.

Start the MCP server

This launches the FastAPI app serving the MCP endpoint at http://127.0.0.1:8000/mcp.

python mcp_server.py

Expected log: uvicorn starts and listens on port 8000.

Health check: MCP client

In a separate terminal (while the server is running), verify connectivity and tool discovery.

python mcp_client_check.py

Expected output: A tools list including ping, add, and subtract.

OpenAI integration test

With the server running and your OpenAI key available, run the LLM orchestration demo:

python mcp_llm_mode2.py

What it does:

• Discovers MCP tools from the server.
• Provides those tools to the OpenAI Chat Completions API.
• Lets the model call add and returns the final answer.

Expected: The printed result of adding two numbers (e.g., 68 for 23 + 45).

Notes

• The script uses model gpt-5. If this model is not available on your account, change it inside call_my_mcp2.py to a model you have access to (e.g., gpt-4.1-mini or another supported tools-capable model).
• Ensure the server URL in call_my_mcp2.py and mcp_client_check.py matches http://127.0.0.1:8000/mcp.
• If port 8000 is in use, edit mcp_server.py to run uvicorn on a different port and update the client scripts accordingly.

Files

• mcp_server.py: FastAPI + FastMCP server exposing tools: ping, add, subtract.
• mcp_client_check.py: Async client that pings the server and lists tools.
• call_my_mcp2.py: Orchestrates an OpenAI chat with MCP tools.
• utils/helpers.py: Utility to load the OpenAI API key from ~/.llm_secrets or set env vars from a JSON file.