MCP Tutorial

Overview

This repository demonstrates a small MCP (Model Context Protocol) ecosystem built around FastMCP. It contains a sample MCP server exposing tools (arXiv fetch, abstract retrieval, save-markdown) and example clients: a simple FastMCP client and a Google Gemini-integrated agent. The code is intended as a practical tutorial and reference for running and testing an MCP server locally, integrating it with LLMs (Gemini), and using the MCP Inspector for interactive exploration.

Key points:

• Server uses the fastmcp library to define and expose tools.
• Client examples show how to call tools programmatically and how to attach an LLM (Gemini) to MCP tools.
• Includes Docker-ready server and agent examples for containerized testing.

Quick start

1. Clone and install dependencies:

   git clone https://github.com/kmkarakaya/mcp_tutorial.git; cd mcp_tutorial
   pip install -r requirements.txt

2. Start the MCP server (local):

   python 4_mcp_server.py

   The server exposes tools and listens for client connections (default behavior provided by FastMCP).

Usage and examples

• Inspect available tools with the official MCP Inspector (requires Node.js and npm or Corepack installed):

  npx @modelcontextprotocol/inspector

  Note: npx comes with npm (or use Corepack on newer Node versions). Install Node.js from https://nodejs.org/ if you don't have node/npm on your system.

• Run the example client to call a tool programmatically:

  python 5_mcp_client.py

• Start an interactive Gemini-based agent that can call MCP tools (requires Google GenAI credentials):

  python 6_mcp_gemini_agent.py

  When using the Gemini agent, set your environment variables per google-genai docs (e.g., GOOGLE_API_KEY or equivalent) before running.

Docker

Dockerfiles/examples are included. Example build/run:

docker build -t mcp_server .
docker run -p 8000:8000 mcp_server

Tools implemented in the server

• fetch_arxiv_papers(topic: str, number_of_papers: int = 3) — fetches recent arXiv papers for a topic.
• get_arxiv_abstract(arxiv_id: str) — retrieves an arXiv paper abstract.
• save_md_to_file(text: str, filename: str) — saves given markdown to ./reports.

Dependencies

See requirements.txt. Important packages:

• fastmcp
• google-genai (for Gemini integration)
• uvicorn (if you run the server via ASGI)

Notes

• The server file (4_mcp_server.py) prints helpful messages when functions are called and includes a mcp.run() entry point.
• Use the MCP Inspector to verify tool metadata and try calls interactively.

A full video tutorial and repository walkthrough is available on the Murat Karakaya Akademi YouTube channel (live recording): https://youtube.com/live/c7yl0GS2mJQ?feature=share

Author

Murat Karakaya
Website
LinkedIn
YouTube

Acknowledgments

Based on MCP work and examples; thanks to Anthropic, OpenAI, and contributors to the MCP ecosystem.