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Build an MCP server

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In this tutorial, we'll build a simple MCP weather server and connect it to a host, Claude for Desktop.

What we'll be building

We'll build a server that exposes two tools: get_alerts and get_forecast. Then we'll connect the server to an MCP host (in this case, Claude for Desktop):

Note

Servers can connect to any client. We've chosen Claude for Desktop here for simplicity, but we also have a guide on building your own client.

Core MCP Concepts

MCP servers can provide three main types of capabilities:

  1. Resources: File-like data that can be read by clients (like API responses or file contents)
  2. Tools: Functions that can be called by the LLM (with user approval)
  3. Prompts: Pre-written templates that help users accomplish specific tasks

This tutorial will primarily focus on tools.

PythonTypeScriptJavaKotlinC#RubyRustGo

Let's get started with building our weather server! You can find the complete code for what we'll be building here.

Prerequisite knowledge

This quickstart assumes you have familiarity with:

  • Python
  • LLMs like Claude

Logging in MCP Servers

When implementing MCP servers, be careful about how you handle logging:

For STDIO-based servers: Never write to stdout. Writing to stdout will corrupt the JSON-RPC messages and break your server. The print() function writes to stdout by default, so keep it out of a STDIO server entirely.

For HTTP-based servers: Standard output logging is fine since it doesn't interfere with HTTP responses.

Best Practices

  • Use the standard library logging module, which writes to stderr.
  • Create one logger per module with logging.getLogger(__name__) and call it from your tools.

Quick Examples

import logging

logger = logging.getLogger(__name__)

# ❌ Bad (STDIO)
print("Processing request")

# ✅ Good (STDIO)
logger.info("Processing request")  # writes to stderr

System requirements

  • Python 3.10 or higher installed.
  • You must use the Python MCP SDK 2.0.0 or higher.

Set up your environment

First, let's install uv and set up our Python project and environment:

macOS/LinuxWindows

curl -LsSf https://astral.sh/uv/install.sh | sh

Make sure to restart your terminal afterwards to ensure that the uv command gets picked up.

Now, let's create and set up our project:

macOS/LinuxWindows

# Create a new directory for our project
uv init weather
cd weather

# Create virtual environment and activate it
uv venv
source .venv/bin/activate

# Install dependencies
uv add "mcp[cli]"

# Create our server file
touch weather.py

Now let's dive into building your server.

Building your server

Importing packages and setting up the instance

Add these to the top of your weather.py:

from typing import Any

import httpx2
from mcp.server import MCPServer

# Initialize MCPServer
mcp = MCPServer("weather")

# Constants
NWS_API_BASE = "https://api.weather.gov"
USER_AGENT = "weather-app/1.0"

httpx2 is the HTTP client the SDK itself depends on, so installing mcp already brought it in.

The MCPServer class uses Python type hints and docstrings to automatically generate tool definitions, making it easy to create and maintain MCP tools.

Helper functions

Next, let's add our helper functions for querying and formatting the data from the National Weather Service API:

async def make_nws_request(url: str) -> dict[str, Any] | None:
    """Make a request to the NWS API with proper error handling."""
    headers = {"User-Agent": USER_AGENT, "Accept": "application/geo+json"}
    async with httpx2.AsyncClient() as client:
        try:
            response = await client.get(url, headers=headers, timeout=30.0)
            response.raise_for_status()
            return response.json()
        except Exception:
            return None


def format_alert(feature: dict) -> str:
    """Format an alert feature into a readable string."""
    props = feature["properties"]
    return f"""
Event: {props.get("event", "Unknown")}
Area: {props.get("areaDesc", "Unknown")}
Severity: {props.get("severity", "Unknown")}
Description: {props.get("description", "No description available")}
Instructions: {props.get("instruction", "No specific instructions provided")}
"""

Implementing tool execution

The tool execution handler is responsible for actually executing the logic of each tool. Let's add it:

@mcp.tool()
async def get_alerts(state: str) -> str:
    """Get weather alerts for a US state.

    Args:
        state: Two-letter US state code (e.g. CA, NY)
    """
    url = f"{NWS_API_BASE}/alerts/active/area/{state}"
    data = await make_nws_request(url)

    if not data or "features" not in data:
        return "Unable to fetch alerts or no alerts found."

    if not data["features"]:
        return "No active alerts for this state."

    alerts = [format_alert(feature) for feature in data["features"]]
    return "\n---\n".join(alerts)


@mcp.tool()
async def get_forecast(latitude: float, longitude: float) -> str:
    """Get weather forecast for a location.

    Args:
        latitude: Latitude of the location
        longitude: Longitude of the location
    """
    # First get the forecast grid endpoint
    points_url = f"{NWS_API_BASE}/points/{latitude},{longitude}"
    points_data = await make_nws_request(points_url)

    if not points_data:
        return "Unable to fetch forecast data for this location."

    # Get the forecast URL from the points response
    forecast_url = points_data["properties"]["forecast"]
    forecast_data = await make_nws_request(forecast_url)

    if not forecast_data:
        return "Unable to fetch detailed forecast."

    # Format the periods into a readable forecast
    periods = forecast_data["properties"]["periods"]
    forecasts = []
    for period in periods[:5]:  # Only show next 5 periods
        forecast = f"""
{period["name"]}:
Temperature: {period["temperature"]}°{period["temperatureUnit"]}
Wind: {period["windSpeed"]} {period["windDirection"]}
Forecast: {period["detailedForecast"]}
"""
        forecasts.append(forecast)

    return "\n---\n".join(forecasts)

Running the server

Finally, let's initialize and run the server:

if __name__ == "__main__":
    mcp.run(transport="stdio")

Your server is complete! Run uv run weather.py to start the MCP server, which will listen for messages from MCP hosts.

Let's now test your server from an existing MCP host, Claude for Desktop.

Testing your server with Claude for Desktop

First, make sure you have Claude for Desktop installed. You can install the latest version here. If you already have Claude for Desktop, make sure it's updated to the latest version.

We'll need to configure Claude for Desktop for whichever MCP servers you want to use. To do this, open your Claude for Desktop App configuration at ~/Library/Application Support/Claude/claude_desktop_config.json in a text editor. Make sure to create the file if it doesn't exist.

For example, if you have VS Code installed:

LinuxmacOSWindows

code ~/.config/Claude/claude_desktop_config.json

You'll then add your servers in the mcpServers key. The MCP UI elements will only show up in Claude for Desktop if at least one server is properly configured.

In this case, we'll add our single weather server like so:

macOS/LinuxWindows

{
  "mcpServers": {
    "weather": {
      "command": "uv",
      "args": [
        "--directory",
        "/ABSOLUTE/PATH/TO/PARENT/FOLDER/weather",
        "run",
        "weather.py"
      ]
    }
  }
}

Warning

You may need to put the full path to the uv executable in the command field. You can get this by running which uv on macOS/Linux or where uv on Windows.

Note

Make sure you pass in the absolute path to your server. You can get this by running pwd on macOS/Linux or cd on Windows Command Prompt. On Windows, remember to use double backslashes (\\) or forward slashes (/) in the JSON path.

This tells Claude for Desktop:

  1. There's an MCP server named "weather"
  2. To launch it by running uv --directory /ABSOLUTE/PATH/TO/PARENT/FOLDER/weather run weather.py

Save the file, and restart Claude for Desktop.

Test with commands

Let's make sure Claude for Desktop is picking up the two tools we've exposed in our weather server. You can do this by looking for the "Add files, connectors, and more /" icon:

After clicking on the plus icon, hover over the "Connectors" menu. You should see the weather servers listed:

If your server isn't being picked up by Claude for Desktop, proceed to the Troubleshooting section for debugging tips.

If the server has shown up in the "Connectors" menu, you can now test your server by running the following commands in Claude for Desktop:

  • What's the weather in Sacramento?
  • What are the active weather alerts in Texas?

Note

Since this is the US National Weather service, the queries will only work for US locations.

What's happening under the hood

When you ask a question:

  1. The client sends your question to Claude
  2. Claude analyzes the available tools and decides which one(s) to use
  3. The client executes the chosen tool(s) through the MCP server
  4. The results are sent back to Claude
  5. Claude formulates a natural language response
  6. The response is displayed to you!

Troubleshooting

Claude for Desktop Integration Issues

Weather API Issues

Note

For more advanced troubleshooting, check out our guide on Debugging MCP

Next steps

Building a client

Learn how to build your own MCP client that can connect to your server

Example servers

Check out our gallery of official MCP servers and implementations

Debugging Guide

Learn how to effectively debug MCP servers and integrations

Build with Agent Skills

Use agent skills to guide AI coding assistants through server design