mcp-weather-sse

This is a tutorial application for setting a Server-Sent Events (SSE) Model Context Protocol (MCP) Server. This server returns weather information for a specified location.

This server demonstrates how to implement a simple MCP SSE server that can be integrated with AI agents.

• SSE-based MCP server implementation
• Real-time weather data retrieval
• Support for current conditions and forecasts
• Easy integration with AI agents through MCP

• Python 3.10 or later
• Node.js and NPM (for the MCP Inspector)
• Git (for version control)

1. Clone the repository:

   git clone [repository-url]
   cd mcp-weather-service

2. Run the setup script:

   scripts/setup.bat

   This will:

   • Create a Python virtual environment in .venv
   • Activate the virtual environment
   • Install all required dependencies

3. (Optional) Configure your IDE to use the Python interpreter from .venv

There are several ways to run the server:

python src/mcp_weather_service/weather_server.py

uvicorn mcp_weather_service.weather_server:app --host 127.0.0.1 --port 3001

scripts/launch.bat

1. Check if the server is running:

   • Open your browser and navigate to http://127.0.0.1:3001/sse
   • You should see a response like:

     event: endpoint
     data: /weather?session_id=f0c33285de5846d79a89f41212acfd75
     

2. Using the MCP Inspector:

   scripts/inspect.bat

   This launches the MCP Protocol Inspector, which allows you to:

   • Interact with the server directly
   • Test weather queries
   • Debug server responses
   • You MUST connect via http://127.0.0.1:3001/sse (use 127.0.0.1 instead of localhost especially on Windows!)

3. Using with a MCP Client There is a gradio MCP Client demo that can be used to interact with this server. See that projects' details to set it up The entry for config.json for THAT project is:

{
  "mcpServers": {
    "weather": {
      "type": "sse",
      "url": "http://127.0.0.1"
    }
  }
}

Gets current weather and forecast for a specified location.

Input Schema:

{
  "location_name": {
    "type": "string",
    "description": "The location to get the weather for"
  }
}

Example Response:

{
  "currently": {
    "current_temperature": 72.5,
    "sky": "☀️",
    "feels_like": 74.2,
    "humidity": 65,
    "wind_speed": 8.5,
    "wind_direction": "NW↗️",
    "visibility": 10,
    "uv_index": 5,
    "description": "Clear skies",
    "forecasts": [
      {
        "date": "2024-12-05",
        "high_temperature": 75.8,
        "low_temperature": 62.4
      }
    ]
  }
}

The project uses a Python virtual environment located in .venv. To activate it manually:

.venv\Scripts\activate  # Windows
source .venv/bin/activate  # Unix/MacOS

1. Add new dependencies to pyproject.toml
2. Run scripts/install_deps.bat to install them

1. Server Won't Start

   • Verify Python version (3.10+)
   • Check if port 3001 is available
   • Ensure virtual environment is activated

2. MCP Inspector Connection Failed

   • Verify the server is running
   • Check the URL (should be http://127.0.0.1:3001/sse)
   • Ensure Node.js and NPM are installed

3. Weather Data Not Returning

   • Check internet connectivity
   • Verify location name format

MIT License - See LICENSE file for details

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a new Pull Request