🌦️ Weather-MCP

🌦️ Weather-MCP

Weather-MCP is a FastAPI server that integrates with FastMCP and Pydantic AI to provide real-time weather alerts and forecasts. Users can interact with the server through natural language prompts, enabling seamless access to weather information.

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📋 Table of Contents

• Features
• Installation
• Usage
• API Endpoints
• Contributing
• License

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🚀 Features

• Fetch active weather alerts for U.S. states.
• Retrieve detailed weather forecasts based on geographic coordinates.
• Natural language interaction powered by Pydantic AI.
• Extensible toolset via FastMCP.

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🛠️ Installation

Prerequisites

• Python 3.8 or higher
• An OpenAI API key (OPENAI_API_KEY): To obtain an OpenAI API key, please visit the OpenAI API Keys page

Ensure you have these prerequisites in place before proceeding with the setup.

Steps

1. Clone the repository:

   git clone https://github.com/yourusername/weather-mcp.git
   cd weather-mcp
   

2. Create and activate a virtual environment:

   pip install uv
   uv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install dependencies:

   uv sync
   

4. Set the OpenAI API Key

To securely use OpenAI services, set the OPENAI_API_KEY environment variable.([DEV Community][1])

On Unix/Linux/macOS:

export OPENAI_API_KEY="your-api-key-here"

To make this change permanent, add the above line to your shell's configuration file (e.g., ~/.bashrc, ~/.zshrc) and reload the shell:([Hacker News][2])

source ~/.bashrc  # or source ~/.zshrc

On Windows (Command Prompt):

setx OPENAI_API_KEY "your-api-key-here"

After setting the variable, restart your Command Prompt to apply the changes.

On Windows (PowerShell):

$env:OPENAI_API_KEY = "your-api-key-here"

5. Start the server:

   python weather_server.py
   

The server will be accessible at http://localhost:8000/sse.

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💡 Usage

Interacting via Pydantic AI

You can interact with the server using natural language prompts through Pydantic AI.([YouTube][1])

Example:

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

# Define the MCP server connection
mcp_server = MCPServerHTTP(url="http://localhost:8000/sse")

# Create the Pydantic AI agent
agent = Agent(
    model="openai:gpt-4o-mini",
    mcp_servers=[mcp_server],
    system_prompt="You are a helpful assistant that provides weather information."
)

async def main():
    prompt = "Are there any weather alerts for California?"
    async with agent.run_mcp_servers():
        result = await agent.run(prompt)
        print(result.output)

if __name__ == "__main__":
    asyncio.run(main())

Direct Tool Invocation

Alternatively, you can directly invoke the tools using FastMCP's client.

Example:

import asyncio
from fastmcp import Client

async def main():
    async with Client("http://localhost:8000/sse") as client:
        tools = await client.list_tools()
        print("Available tools:")
        for tool in tools:
            print(f"- {tool.name}: {tool.description}")

        # Call the get_weather_alerts tool
        state_code = "CA"
        alerts_result = await client.call_tool("get_weather_alerts", {"state": state_code})
        print(f"\nWeather alerts for {state_code}:\n{alerts_result[0].text}")

        # Call the get_weather_forecast tool
        latitude = 34.05
        longitude = -118.25
        forecast_result = await client.call_tool("get_weather_forecast", {
            "latitude": latitude,
            "longitude": longitude
        })
        print(f"\nWeather forecast for ({latitude}, {longitude}):\n{forecast_result[0].text}")

if __name__ == "__main__":
    asyncio.run(main())

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📡 API Endpoints

The server exposes the following tools via the MCP protocol:

• get_weather_alerts(state: str) -> str Fetches active weather alerts for the specified U.S. state.

• get_weather_forecast(latitude: float, longitude: float) -> str Retrieves the weather forecast for the given geographic coordinates.

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🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch: git checkout -b feature-name.
3. Make your changes and commit them: git commit -m 'Add feature'.
4. Push to the branch: git push origin feature-name.
5. Open a pull request.([GitHub][2])

For major changes, please open an issue first to discuss what you would like to change.([Make a README][3])

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📄 License

This project is licensed under the MIT License.