Weather MCP Server Pyp

🚀 Weather MCP Server

This is a learning project aimed at exploring the concept of Model Context Protocol (MCP). It implements a simple weather service that uses the National Weather Service (NWS) API to provide weather alerts and forecasts.

🚀 Quick Start

The Weather MCP Server is a learning project designed to explore the Model Context Protocol (MCP). It offers a simple weather service by leveraging the National Weather Service (NWS) API to provide weather alerts and forecasts.

✨ Features

• get_alerts: Retrieve current weather alerts for a US state.
• get_forecast: Obtain a weather forecast for a specific location based on latitude and longitude.

📦 Installation

Requirements

• Python 3.9+
• httpx library
• mcp library

Steps

1. Clone this repository.
2. Install the dependencies:

   pip install mcp httpx
   

   Or use uv:

   uv pip install mcp httpx
   

3. Configure the MCP server in your AI assistant:

In Claude Desktop

Add the following to your Claude Desktop configuration:

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

In Cursor

Go to Cursor settings -> MCP servers -> Add MCP server

• Name: Weather (or any name you like)
• Type: Command
• Command: uv run /ABSOLUTE/PATH/TO/PARENT/FOLDER/weather/weather.py

💻 Usage Examples

Basic Usage

Once the MCP server is set up, you can ask your AI assistant:

• "Are there any current weather alerts in CA?"
• "Get the weather forecast for latitude 37.7749 and longitude -122.4194"

📚 Documentation

This project was created as an exercise to learn MCP, following the documentation at: https://modelcontextprotocol.io/quickstart/server#core-mcp-concepts MCP is a protocol that allows AI assistants to access external tools and data sources. This project demonstrates how to create an MCP server for Claude or other MCP-compatible assistants.

📄 License

This project is for educational purposes.

Acknowledgments

Thanks to the National Weather Service (NWS) for providing weather data and the MCP documentation for guiding the implementation of the server.