Open-Meteo MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPEN_METEO_API_URL | No | Base URL for Open-Meteo forecast API | https://api.open-meteo.com |
| OPEN_METEO_MARINE_API_URL | No | Marine weather API URL | https://marine-api.open-meteo.com |
| OPEN_METEO_ARCHIVE_API_URL | No | Historical data API URL | https://archive-api.open-meteo.com |
| OPEN_METEO_ENSEMBLE_API_URL | No | Ensemble forecast API URL | https://ensemble-api.open-meteo.com |
| OPEN_METEO_SEASONAL_API_URL | No | Seasonal forecast API URL | https://seasonal-api.open-meteo.com |
| OPEN_METEO_GEOCODING_API_URL | No | Geocoding API URL | https://geocoding-api.open-meteo.com |
| OPEN_METEO_AIR_QUALITY_API_URL | No | Air quality API URL | https://air-quality-api.open-meteo.com |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| weather_forecastC | Get weather forecast data for coordinates using Open-Meteo API. Supports hourly and daily data with various weather variables. |
| weather_archiveA | Get historical weather data from ERA5 reanalysis (1940-present) for specific coordinates and date range. |
| air_qualityB | Get air quality forecast data including PM2.5, PM10, ozone, nitrogen dioxide and other pollutants. |
| marine_weatherB | Get marine weather forecast including wave height, wave period, wave direction and sea surface temperature. |
| elevationA | Get elevation data for given coordinates using digital elevation models. |
| flood_forecastB | Get river discharge and flood forecasts from GloFAS (Global Flood Awareness System). |
| seasonal_forecastC | Get long-range seasonal forecasts for temperature and precipitation up to 9 months ahead. |
| climate_projectionC | Get climate change projections from CMIP6 models for different warming scenarios. |
| ensemble_forecastC | Get ensemble forecasts showing forecast uncertainty with multiple model runs. |
| geocodingA | Search for locations worldwide by place name or postal code. Returns geographic coordinates (latitude and longitude) and detailed location information. Use this tool when you need to convert a location name (e.g., "Paris", "New York") into precise coordinates (latitude/longitude) that are required by other tools. This is essential when you have a location name but need coordinates for data fetching tools. |
| dwd_icon_forecastB | Get weather forecast from German DWD ICON model with high resolution data for Europe and global coverage. |
| gfs_forecastB | Get weather forecast from US NOAA GFS model with global coverage and high-resolution data for North America. |
| meteofrance_forecastB | Get weather forecast from French Météo-France models including AROME (high-resolution France) and ARPEGE (Europe). |
| ecmwf_forecastB | Get weather forecast from European Centre for Medium-Range Weather Forecasts with high-quality global forecasts. |
| jma_forecastB | Get weather forecast from Japan Meteorological Agency with high-resolution data for Japan and Asia. |
| metno_forecastC | Get weather forecast from Norwegian weather service with high-resolution data for Nordic countries. |
| gem_forecastC | Get weather forecast from Canadian weather service GEM model with high-resolution data for Canada and North America. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 17 tools
Each tool has a clearly distinct purpose, targeting specific weather or climate data sources, models, or functions. For example, 'air_quality' focuses on pollutants, 'flood_forecast' on river discharge, and 'geocoding' on location conversion, with no overlap in their core functionalities. The descriptions explicitly differentiate them, making misselection unlikely.
All tool names follow a consistent snake_case pattern with a clear noun-based structure (e.g., 'air_quality', 'climate_projection', 'geocoding'). There are no deviations in naming conventions, making the set predictable and easy to parse for agents. This uniformity enhances usability and reduces cognitive load.
With 17 tools, the count is slightly high but reasonable for a comprehensive weather and climate data server covering multiple models, forecasts, and auxiliary functions. Each tool serves a specific niche, such as different regional forecasts or data types, justifying its inclusion without appearing overly bloated. A minor reduction could improve focus, but it's well within an acceptable range.
The tool surface is highly complete for the domain of weather and climate data, offering extensive coverage including forecasts from various global models (e.g., ECMWF, GFS), specialized data (e.g., air quality, floods), historical archives, and essential utilities like geocoding. There are no obvious gaps; agents can perform a full range of data retrieval and conversion tasks seamlessly.