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Build Your First MCP Server in Python in 10 Minutes

python dev.to

MCP (Model Context Protocol) crossed 97 million monthly installs in March 2026 — faster adoption than React hit in its first three years. Chrome just shipped a DevTools MCP server. Microsoft Fabric went GA with MCP support. The ecosystem is converging fast, and if you want your AI agents to talk to anything — a database, an API, a local tool — writing an MCP server is the skill everyone needs right now.

This tutorial skips the theory. In under 800 words and one Python file, you'll have a working MCP server that your AI coding assistant can call.

What You're Building

A simple MCP server that exposes two tools:

  • get_weather(city) — returns mock weather data for a city (replace with a real API call later)
  • convert_currency(amount, from_currency, to_currency) — returns mock exchange rates

Your AI agent (Claude, Cursor, Copilot, or any MCP client) will see these tools listed and call them autonomously.

Prerequisites 

pip install fastmcp
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That's it. FastMCP is the fastest way to write MCP servers in Python. It wraps the official mcp SDK and handles stdio transport, JSON-RPC, and tool registration automatically.

The Code

Create weather_server.py:

from fastmcp import FastMCP

# Initialize the server
mcp = FastMCP("weather-tools", port=8000)

# Mock data — swap these out for real API calls
WEATHER = {
    "london": {"temp_c": 12, "condition": "cloudy", "wind_kmh": 18},
    "tokyo": {"temp_c": 22, "condition": "clear", "wind_kmh": 8},
    "new york": {"temp_c": 18, "condition": "rainy", "wind_kmh": 25},
}

RATES = {
    ("usd", "eur"): 0.92,
    ("usd", "gbp"): 0.79,
    ("eur", "usd"): 1.09,
    ("gbp", "usd"): 1.27,
}

# Tool 1: Weather lookup
@mcp.tool()
def get_weather(city: str) -> str:
    """Get current weather for a city. Returns temperature in Celsius, condition, and wind speed."""
    data = WEATHER.get(city.lower())
    if not data:
        return f"No weather data for {city}. Supported cities: {', '.join(WEATHER.keys())}"
    return f"{city.title()}: {data['temp_c']}\u00b0C, {data['condition']}, wind {data['wind_kmh']} km/h"

# Tool 2: Currency converter
@mcp.tool()
def convert_currency(amount: float, from_currency: str, to_currency: str) -> str:
    """Convert an amount between currencies. Supports USD, EUR, GBP."""
    key = (from_currency.lower(), to_currency.lower())
    rate = RATES.get(key)
    if not rate:
        return f"No rate for {from_currency.upper()} → {to_currency.upper()}. Supported: USD, EUR, GBP"
    result = amount * rate
    return f"{amount}{from_currency.upper()} = {result:.2f}{to_currency.upper()}"

if __name__ == "__main__":
    mcp.run()
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Run it:

python weather_server.py
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The server starts on http://localhost:8000/mcp using Streamable HTTP transport (the MCP default since v2025.03.26 of the spec).

Connect It to Your AI Client

Claude Desktop

Add this to your Claude Desktop config (claude_desktop_config.json):

{"mcpServers":{"weather-tools":{"command":"python","args":["/path/to/weather_server.py"]}}}
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Restart Claude Desktop. When you ask "What's the weather in Tokyo?" or "Convert 100 USD to EUR," Claude will see the tools and call them directly.

Cursor / VS Code Copilot

Add to your .mcp.json or MCP settings:

{"mcpServers":{"weather-tools":{"command":"python","args":["${workspaceFolder}/weather_server.py"]}}}
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Programmatic (Python client) 

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

async def main():
    params = StdioServerParameters(
        command="python", args=["weather_server.py"]
    )
    async with stdio_client(params) as streams:
        async with ClientSession(*streams) as session:
            await session.initialize()
            tools = await session.list_tools()
            print([t.name for t in tools.tools])
            # ['get_weather', 'convert_currency']

import asyncio
asyncio.run(main())
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What Just Happened

  1. @mcp.tool() decorator registers each function as an MCP tool. FastMCP auto-generates the JSON Schema from Python type hints — no manual tool definitions needed.
  2. Docstrings become tool descriptions. The AI reads these to decide when to call each tool. Write clear, specific descriptions — they're your tool's API docs.
  3. Type hints become validation. If a client passes a string where a float is expected, MCP returns a structured error before your code even runs.
  4. Stdio transport means Claude/Cursor/VS Code spawn your script as a subprocess. No network ports, no auth — it's the simplest and most secure transport for local dev.

Next Steps

Replace the mock data with real API calls:

import httpx

@mcp.tool()
def get_weather(city: str) -> str:
    """Get current weather for a city using OpenWeatherMap API."""
    resp = httpx.get(
        f"https://api.openweathermap.org/data/2.5/weather",
        params={"q": city, "appid": "YOUR_API_KEY", "units": "metric"}
    )
    data = resp.json()
    return f"{city.title()}: {data['main']['temp']}\u00b0C, {data['weather'][0]['description']}"
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You can also add tools that write files, query databases, call internal APIs, or trigger CI/CD pipelines. The pattern is the same: decorate a function, write a clear docstring, and your AI agent gains a new capability.

The official MCP specification and FastMCP docs cover advanced patterns: resources, prompts, progress reporting, and OAuth authentication — all worth exploring once your first server is running.

TL;DR: MCP isn't a framework to learn — it's a protocol to connect. One Python file, three lines per tool, and your AI agent suddenly has hands.

Source: dev.to

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