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Open-Meteo

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Access global weather forecasts and historical data through the Open-Meteo API.

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Open-Meteo MCP Server

npm version GitHub release Docker Image

A comprehensive Model Context Protocol (MCP) server that provides access to Open-Meteo weather APIs for use with Large Language Models.

Features

This MCP server provides complete access to Open-Meteo APIs, including:

Core Weather APIs

  • Weather Forecast (weather_forecast) - 7-day forecasts with hourly and daily resolution
  • Weather Archive (weather_archive) - Historical ERA5 data from 1940 to present
  • Air Quality (air_quality) - PM2.5, PM10, ozone, nitrogen dioxide, pollen, European/US AQI indices, UV index and other pollutants
  • Marine Weather (marine_weather) - Wave height, wave period, wave direction and sea surface temperature
  • Elevation (elevation) - Digital elevation model data for given coordinates
  • Geocoding (geocoding) - Search locations worldwide by name or postal code, get coordinates and detailed location information

Specialized Weather Models

  • DWD ICON (dwd_icon_forecast) - German weather service high-resolution model for Europe
  • NOAA GFS (gfs_forecast) - US weather service global model with high-resolution North America data
  • Météo-France (meteofrance_forecast) - French weather service AROME and ARPEGE models
  • ECMWF (ecmwf_forecast) - European Centre for Medium-Range Weather Forecasts
  • JMA (jma_forecast) - Japan Meteorological Agency high-resolution model for Asia
  • MET Norway (metno_forecast) - Norwegian weather service for Nordic countries
  • Environment Canada GEM (gem_forecast) - Canadian weather service model

Advanced Forecasting Tools

  • Flood Forecast (flood_forecast) - River discharge and flood forecasts from GloFAS (Global Flood Awareness System)
  • Seasonal Forecast (seasonal_forecast) - Long-range forecasts up to 9 months ahead
  • Climate Projections (climate_projection) - CMIP6 climate change projections for different warming scenarios
  • Ensemble Forecast (ensemble_forecast) - Multiple model runs showing forecast uncertainty

Skills

The skills/ directory contains SKILL.md files that help AI assistants use this MCP server effectively. They act as contextual guides — the AI reads the relevant skill to know which tool to call and how to use its parameters.

Available skills

SkillFileBest for
open-meteoskills/open-meteo/SKILL.mdEveryday weather: forecasts, historical data, air quality, marine conditions, elevation
open-meteo-advancedskills/open-meteo-advanced/SKILL.mdSpecific models (ECMWF, GFS, DWD ICON…), ensemble uncertainty, seasonal outlooks, climate projections

Using with Claude Code (CLI)

Copy the skill(s) to your Claude skills directory:

Copy & paste — that's it
cp -r skills/open-meteo ~/.claude/skills/
cp -r skills/open-meteo-advanced ~/.claude/skills/

This installs them at ~/.claude/skills/open-meteo/SKILL.md and ~/.claude/skills/open-meteo-advanced/SKILL.md. Claude Code will load the relevant skill automatically when you ask weather-related questions.

Using with Claude Desktop

Upload the SKILL.md file directly as a document in your Claude Desktop conversation:

  • For everyday weather questions: upload skills/open-meteo/SKILL.md
  • For model selection, ensemble, or climate projections: upload skills/open-meteo-advanced/SKILL.md

Upload one skill per conversation. The AI will use it as a reference guide throughout the session.

API Parameters

Required Parameters

  • latitude : Latitude in WGS84 coordinate system (-90 to 90)
  • longitude : Longitude in WGS84 coordinate system (-180 to 180)

Hourly Weather Variables

  • temperature_2m : Temperature at 2 meters
  • relative_humidity_2m : Relative humidity
  • precipitation : Precipitation
  • wind_speed_10m : Wind speed at 10 meters
  • wind_direction_10m : Wind direction
  • pressure_msl : Mean sea level pressure
  • cloud_cover : Cloud cover percentage
  • weather_code : Weather condition code
  • visibility : Visibility
  • uv_index : UV index
  • And many more...

Daily Weather Variables

  • temperature_2m_max/min : Maximum/minimum temperatures
  • precipitation_sum : Total precipitation
  • wind_speed_10m_max : Maximum wind speed
  • sunrise/sunset : Sunrise and sunset times
  • weather_code : Weather condition code
  • uv_index_max : Maximum UV index

Air Quality Variables

  • pm10 : PM10 particles
  • pm2_5 : PM2.5 particles
  • carbon_monoxide : Carbon monoxide
  • nitrogen_dioxide : Nitrogen dioxide
  • ozone : Ozone
  • sulphur_dioxide : Sulfur dioxide
  • ammonia : Ammonia
  • dust : Dust particles
  • alder_pollen : Alder pollen (Europe only)
  • birch_pollen : Birch pollen (Europe only)
  • grass_pollen : Grass pollen (Europe only)
  • mugwort_pollen : Mugwort pollen (Europe only)
  • olive_pollen : Olive pollen (Europe only)
  • ragweed_pollen : Ragweed pollen (Europe only)
  • european_aqi : European Air Quality Index
  • european_aqi_pm2_5 : European AQI for PM2.5
  • european_aqi_pm10 : European AQI for PM10
  • european_aqi_nitrogen_dioxide : European AQI for NO₂
  • european_aqi_ozone : European AQI for ozone
  • european_aqi_sulphur_dioxide : European AQI for SO₂
  • us_aqi : US Air Quality Index
  • us_aqi_pm2_5 : US AQI for PM2.5
  • us_aqi_pm10 : US AQI for PM10
  • us_aqi_nitrogen_dioxide : US AQI for NO₂
  • us_aqi_ozone : US AQI for ozone
  • us_aqi_sulphur_dioxide : US AQI for SO₂
  • us_aqi_carbon_monoxide : US AQI for CO
  • uv_index : UV index
  • uv_index_clear_sky : UV index under clear sky conditions

Marine Weather Variables

  • wave_height : Wave height
  • wave_direction : Wave direction
  • wave_period : Wave period
  • wind_wave_height : Wind wave height
  • swell_wave_height : Swell wave height
  • sea_surface_temperature : Sea surface temperature

Formatting Options

  • temperature_unit : celsius, fahrenheit
  • wind_speed_unit : kmh, ms, mph, kn
  • precipitation_unit : mm, inch
  • timezone : Europe/Paris, America/New_York, etc.

Time Range Options

  • forecast_days : Number of forecast days (varies by API)
  • past_days : Include past days data
  • start_date / end_date : Date range for historical data (YYYY-MM-DD format)

Development Scripts

Copy & paste — that's it
# Development with auto-reload
npm run dev

# Build TypeScript
npm run build

# Start production server
npm start

# Run tests
npm test

# Type checking
npm run typecheck

# Linting
npm run lint

Project Structure

Copy & paste — that's it
src/
├── index.ts          # MCP server entry point
├── client.ts         # HTTP client for Open-Meteo API
├── tools.ts          # MCP tool definitions
├── types.ts          # Zod validation schemas
└── security.ts       # Auth middleware, rate limiter, IP extraction

API Coverage

This server provides access to all major Open-Meteo endpoints:

Weather Data

  • Current weather conditions
  • Hourly forecasts (up to 16 days)
  • Daily forecasts (up to 16 days)
  • Historical weather data (1940-present)

Specialized Models

  • High-resolution regional models (DWD ICON, Météo-France AROME)
  • Global models (NOAA GFS, ECMWF)
  • Regional specialists (JMA for Asia, MET Norway for Nordics)

Environmental Data

  • Air quality forecasts
  • Marine and ocean conditions
  • River discharge and flood warnings
  • Climate change projections

Advanced Features

  • Ensemble forecasts for uncertainty quantification
  • Seasonal forecasts for long-term planning
  • Multiple model comparison
  • Customizable units and timezones

Error Handling

The server provides comprehensive error handling with detailed error messages for:

  • Invalid coordinates
  • Missing required parameters
  • API rate limits
  • Network connectivity issues
  • Invalid date ranges

Performance

  • Efficient HTTP client with connection pooling
  • Optimized data serialization
  • Minimal memory footprint

API Documentation

For detailed API documentation, refer to the openapi.yml file and the Open-Meteo API documentation.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Development Setup

  1. Fork the repository
  2. Clone your fork: git clone https://github.com/your-username/open-meteo-mcp.git
  3. Install dependencies: npm install
  4. Create a feature branch: git checkout -b feature/amazing-feature
  5. Make your changes and add tests
  6. Run tests: npm test
  7. Commit your changes: git commit -m 'Add amazing feature'
  8. Push to the branch: git push origin feature/amazing-feature
  9. Open a Pull Request

Releasing

This project uses automated releases via GitHub Actions. To create a new release:

Copy & paste — that's it
# For a patch release (1.0.0 -> 1.0.1)
npm run release:patch

# For a minor release (1.0.0 -> 1.1.0)
npm run release:minor

# For a major release (1.0.0 -> 2.0.0)
npm run release:major

The GitHub Action will automatically:

  • Run tests and build the project
  • Publish to npm with provenance
  • Create a GitHub release
  • Update version badges

License

MIT