FastMCP 101

Build, test, and deploy your first MCP server in Python. No prior MCP experience needed.

The Model Context Protocol (MCP) is the shared standard that lets AI assistants use outside systems: your data, your APIs, your expertise. You build a server that offers capabilities; any MCP client (Claude, ChatGPT, Cursor, an agency chatbot) can then use them. Think of it as the wall outlet between assistants and the systems you own. The utility doesn't care what you plug in.

FastMCP is the Python framework that makes a server out of ordinary functions. One decorator per function. This repo pins FastMCP 4.

What you'll build

A weather server with three tools, one resource, and one prompt, backed by Open-Meteo (free, no API key). By the end you will have run it locally, tested it in memory, and deployed it to a public URL on Prefect Horizon.

The lessons live in WORKSHOP.md. Follow them in order.

Prefer a browser sandbox with nothing to install? The same course runs on Instruqt: FastMCP Foundations.

Related MCP server: wetter-mcp-server

Prerequisites

• Python 3.10 or newer (3.12 recommended)

• Git

• A GitHub account (Lesson 5 only)

• Any editor. VS Code, Cursor, and PyCharm all work.

Setup

Clone the repo, then pick one of the two install paths.

git clone https://github.com/prefectlabs/fastmcp-101.git
cd fastmcp-101

Option A: uv (fastest, and what the lessons assume)

# Install uv once if you don't have it: https://docs.astral.sh/uv/
uv sync
source .venv/bin/activate        # Windows: .venv\Scripts\activate

Option B: plain venv and pip

python -m venv .venv
source .venv/bin/activate        # Windows: .venv\Scripts\activate
pip install -r requirements.txt

Confirm it worked:

fastmcp version

You should see FastMCP version: 4.x. Now open WORKSHOP.md and start Lesson 1.

Repo layout

fastmcp-101/
├── weather_server/
│   └── server.py        # the starter: three functions waiting for @mcp.tool
├── solutions/
│   ├── hello.py         # Lesson 1 answer
│   └── server.py        # finished server: tools, resource, prompt
├── tests/
│   └── test_server.py   # in-memory tests you run in Lesson 4
├── WORKSHOP.md          # the five lessons
├── pyproject.toml       # dependencies (FastMCP 4, requests, pytest)
└── requirements.txt     # same dependencies for plain pip

The starter server intentionally fails its tests until you finish Lessons 2 and 3. If you get stuck, diff your file against solutions/server.py.

Handy commands

fastmcp list weather_server/server.py --resources --prompts   # what the AI sees
fastmcp call weather_server/server.py get_weather_forecast location=Denver days_ahead=3
fastmcp inspect weather_server/server.py:mcp                  # summary a deploy platform sees
pytest                                                        # offline tests
pytest -m integration                                         # also hit the live API
fastmcp run weather_server/server.py:mcp --transport http --port 8000   # serve over HTTP

Deploying

Lesson 5 walks through it. The short version: push this repo to your own GitHub, sign in at horizon.prefect.io, pick the repo, set the entrypoint to weather_server/server.py:mcp, and click Deploy. Your server gets a URL like https://<your-server-name>.fastmcp.app/mcp and redeploys on every push to main.

Connecting a client

Once deployed, any MCP client can use your server. For Claude Code:

claude mcp add --transport http weather https://<your-server-name>.fastmcp.app/mcp

For Cursor, Claude Desktop, and others, Horizon shows a copy-paste connection snippet on your server's page.

Going further

• FastMCP docs for everything past 101: auth, middleware, background tasks, interactive apps

• MCP specification

• Deploying to Prefect Horizon

License

Apache 2.0. See LICENSE.