Weather MCP Server

A Model Context Protocol (MCP) server that exposes weather forecasting tools for Databricks Agent Bricks. Built with FastMCP and OpenMeteo's free weather API.

Features

MCP Tools

1. get_forecast(location, days) - Get hourly weather forecast for the next X days

   • Temperature, humidity, and wind speed data

   • Returns structured data with coordinates and forecast period

2. get_current_weather(location) - Get current weather conditions

   • Real-time temperature, wind speed, and weather conditions

   • Includes location coordinates and timestamp

3. predict_umbrella_needed(location, date) - Intelligent umbrella recommendation

   • Analyzes precipitation probability, rainfall amount, and duration

   • Returns YES/MAYBE/NO recommendation with detailed reasoning

   • Includes confidence level (high/medium/low)

4. umbrella prediction logic explanation

   Related MCP server: Weather Prediction MCP Server

   Umbrella Recommendation Logic

The predict_umbrella_needed tool uses a multi-factor decision algorithm to recommend whether you should bring an umbrella.

Thresholds and Decision Factors

Decision Algorithm

The tool evaluates all four factors and makes a recommendation:

• YES - Bring an umbrella ☔: If any of the following are true:

  • Precipitation probability > 70%

  • Expected rainfall > 5mm

  • Precipitation expected for > 6 hours

  • Rain or thunderstorm detected

• MAYBE - Consider bringing one: If:

  • Expected rainfall 1-5mm AND precipitation probability 40-70%

  • Snow conditions detected (umbrella may help but not ideal)

  • Borderline conditions between YES and NO

• NO - Umbrella not needed ☀️: If all factors below thresholds:

  • Precipitation probability < 40%

  • Expected rainfall < 1mm

  • No rain/thunderstorm conditions detected

Confidence Levels

• High: Clear decision based on strong rain signals or confirmed weather codes

• Medium: Moderate precipitation probability (40-70%) with moderate rainfall

• Low: Marginal or unavailable data; recommendation is less certain

Example Scenarios

Additional Features

• Automatic tracing - All MCP calls are logged to Lakebase with session IDs, timing, and results

• User identity tracking - Captures end-user email from Databricks App headers

• Error handling - Comprehensive error handling with structured error responses

• Geocoding - Automatic city name to coordinates conversion using OpenStreetMap

Project Structure

weather-mcp-server/
├── mcp_server/
│   ├── openmeteo_mcp_server.py   # FastMCP server with tool definitions
│   ├── openmeteo_broker.py       # Weather API client functions
│   ├── lakebase.py                # Database connection utilities
│   ├── app.yaml                   # Databricks App configuration
│   └── requirements.txt           # Python dependencies

└── README.md                      # This file

Setup

1. Install Dependencies

pip install -r requirements.txt

2. Configure Environment Variables

Create a .env file in the project root:

LAKEBASE_URL="postgresql://user:password@host.cloud.databricks.com/databricks_postgres?sslmode=require"

3. Test Locally

python -m mcp_server.openmeteo_mcp_server

The server will start on port 8000 and initialize the weather_mcp_traces table in Lakebase.

Deployment as Databricks App

Option 1: Using Databricks CLI

# Ensure LAKEBASE_URL is set in your environment
export LAKEBASE_URL="your-connection-string"

# Deploy the app
databricks apps deploy weather-mcp-server

Option 2: Using Databricks Workspace UI

1. Go to Apps in your Databricks workspace

2. Click Create App

3. Select this directory: /Users/madanadi0305@gmail.com/weather-mcp-server

4. Databricks will automatically detect app.yaml and deploy

Register with Agent Bricks

Once deployed, register the MCP server with your Agent Bricks agent:

1. Get the app URL from the Databricks Apps console

2. In Agent Bricks, add external MCP server:

   • URL: https://<your-app-url>

   • Name: weather-mcp-server

Usage Examples

Get 7-Day Forecast

result = get_forecast("London", 7)
print(result["location"])  # "London"
print(result["coordinates"])  # {"latitude": 51.5074, "longitude": -0.1278}
print(result["data"]["hourly"]["temperature_2m"][0])  # 15.2

Get Current Weather

result = get_current_weather("Tokyo")
current = result["data"]["current_weather"]
print(f"Temperature: {current['temperature']}°C")  # Temperature: 18.5°C

Check If Umbrella Needed

result = predict_umbrella_needed("Seattle", "2024-03-20")
print(result["recommendation"])  # "YES - Bring an umbrella ☔"
print(result["reasoning"])       # "High precipitation probability (85%)..."
print(result["confidence"])      # "high"

Database Schema

The server automatically creates a weather_mcp_traces table in Lakebase:

CREATE TABLE weather_mcp_traces (
    session_id VARCHAR(36) PRIMARY KEY,
    tool_name VARCHAR(100) NOT NULL,
    user_email VARCHAR(255),
    input_params JSONB,
    start_time TIMESTAMP NOT NULL,
    end_time TIMESTAMP,
    duration_ms INTEGER,
    status VARCHAR(20),
    error_message TEXT,
    result_summary JSONB,
    created_at TIMESTAMP DEFAULT NOW()
)

API Documentation

OpenMeteo API

This server uses two OpenMeteo endpoints:

• Current Weather: https://api.open-meteo.com/v1/forecast

• Forecast: https://historical-forecast-api.open-meteo.com/v1/forecast

Both are free and require no API key.

Geocoding

City-to-coordinates conversion uses OpenStreetMap's Nominatim API:

• Endpoint: https://nominatim.openstreetmap.org/search

• Free, no API key required

• Respects usage policies with proper User-Agent header

Development

Running Tests

# Test database connection
python mcp_server/lakebase.py

# Test weather API functions
python mcp_server/openmeteo_broker.py

Adding New Tools

To add a new MCP tool:

1. Add the function to openmeteo_broker.py

2. Wrap it as an MCP tool in openmeteo_mcp_server.py:

@mcp.tool
@trace_mcp_call
def my_new_tool(param: str) -> dict:
    """Tool description for Agent Bricks."""
    return openmeteo_broker.my_new_function(param)

Troubleshooting

Connection Issues

• Verify LAKEBASE_URL is set correctly in .env

• Test connection: python mcp_server/lakebase.py

• Check firewall/security group settings

Import Errors

• Ensure all dependencies are installed: pip install -r requirements.txt

• Verify you're in the correct directory when running

MCP Server Not Responding

• Check logs in Databricks Apps console

• Verify port 8000 is accessible

• Test locally first before deploying

Known Issues

User Identity Tracking

• The RequestContextMiddleware is currently disabled due to FastMCP validation issues

• This means user_email field in weather_mcp_traces table will be NULL

• Impact: Cannot track which end-user made each MCP call

• Status: Investigating FastMCP-compatible middleware approach

Workaround Options

1. Add user context to tool parameters: Modify tools to accept optional user_email parameter

2. Use session-based tracking: Track sessions instead of individual users

3. Wait for FastMCP middleware fix: Monitor FastMCP updates for middleware compatibility

Performance Notes

• Geocoding cache: City-to-coordinates lookups are cached in memory for the app lifetime

• API rate limits: OpenMeteo and Nominatim are free services with fair-use policies

• Database performance: Each MCP call writes one trace record to Lakebase (async recommended)

Security Considerations

• LAKEBASE_URL: Contains database credentials - keep .env file secure and out of version control

• MCP endpoint: Publicly accessible at /mcp - authentication handled by Databricks Apps OAuth

• User headers: The app receives x-forwarded-email from Databricks - trust this for identity

Next Steps

• Re-enable user tracking with FastMCP-compatible middleware

• Add caching layer for weather API responses

• Implement additional weather tools (air quality, UV index, etc.)

• Add monitoring and alerting for API failures

• Create automated tests for all three tools

License

MIT License - see LICENSE file for details

Contributing

Contributions welcome! Please open an issue or pull request.

Support

For issues or questions:

• Check the Troubleshooting section above

• Review logs: databricks apps logs mcp-server-openmeteo-weather

• Open an issue in the project repository