Tools

Ten tools covering geocoding, weather forecasts, historical climate, probabilistic ensemble forecasts, marine conditions, air quality, terrain elevation, river discharge, and SQL analytics over large datasets:

openmeteo_geocode

Resolve a free-text place name to ranked coordinate matches. Required first step for name-based queries — all weather tools accept latitude/longitude, not place names.

• Returns name, country, admin1/admin2, latitude, longitude, elevation, IANA timezone, population, and GeoNames feature code
• Up to 10 ranked matches (default 5), most relevant first — use count for disambiguation when common names (e.g., "Springfield") may match multiple cities
• Pass the timezone from a geocode result directly to weather tools as the timezone parameter
• Returns an empty results array (not an error) when no places match

openmeteo_get_forecast

Weather forecast for a coordinate pair with hourly and/or daily variable selection.

• Up to 16 forecast days ahead (forecast_days 1–16, default 7)
• past_days (0–92) covers recent history via the forecast model — use instead of openmeteo_get_historical for dates within the last ~5 days to avoid ERA5 lag
• Common hourly variables: temperature_2m, precipitation, wind_speed_10m, relative_humidity_2m, cloud_cover, uv_index, apparent_temperature, precipitation_probability, weather_code, surface_pressure, visibility, wind_direction_10m, wind_gusts_10m, dew_point_2m
• Common daily variables: temperature_2m_max, temperature_2m_min, precipitation_sum, wind_speed_10m_max, sunrise, sunset, uv_index_max, precipitation_hours, weather_code
• At least one of hourly_variables or daily_variables is required
• Configurable temperature unit (Celsius/Fahrenheit), wind speed unit (km/h, mph, m/s, knots), and precipitation unit (mm/inch)
• Reshapes the API's columnar response into per-timestamp records with a parallel hourly_units / daily_units map

openmeteo_get_historical

Historical weather from the ERA5 reanalysis archive, covering 1940 to approximately 5 days ago.

• Requires start_date and end_date (YYYY-MM-DD); ERA5 has a variable ~1–5 day lag
• Same variable vocabulary as openmeteo_get_forecast — past and forecast data are directly comparable on one schema
• At least one of hourly_variables or daily_variables is required
• Large date ranges (multi-year hourly queries) spill to DataCanvas when CANVAS_PROVIDER_TYPE=duckdb — output includes canvas_id and truncated: true when the inline record limit is exceeded
• Spill → query workflow: call openmeteo_dataframe_describe with the canvas_id to list tables, then openmeteo_dataframe_query to run SQL SELECT against the staged data

openmeteo_get_marine

Marine weather forecast for coastal and open-ocean coordinates.

• Up to 7 forecast days (forecast_days 1–7, default 7)
• Common hourly variables: wave_height, wave_direction, wave_period, wind_wave_height, wind_wave_direction, wind_wave_period, swell_wave_height, swell_wave_direction, swell_wave_period
• Common daily variables: wave_height_max, wave_direction_dominant, wave_period_max
• At least one of hourly_variables or daily_variables is required
• Inland or sheltered-water points return near-zero wave values (physically correct); ocean_current_velocity is null for non-open-ocean coordinates

openmeteo_get_air_quality

Modeled CAMS air quality forecast. Forecast-only — there is no historical archive for CAMS data.

• Up to 7 forecast days (forecast_days 1–7, default 5)
• Common variables: pm2_5, pm10, carbon_monoxide, nitrogen_dioxide, sulphur_dioxide, ozone, dust, european_aqi, us_aqi, alder_pollen, birch_pollen, grass_pollen, mugwort_pollen, olive_pollen, ragweed_pollen
• At least one variable from hourly_variables is required
• Grid-modeled data from CAMS — resolution is coarser than ground stations; for measured station readings, cross-reference openaq-mcp-server
• Output includes data_source: "CAMS" to distinguish modeled from measured data

openmeteo_get_elevation

Terrain elevation from the Copernicus Digital Elevation Model (~90m resolution).

• Accepts parallel latitudes[] and longitudes[] arrays; both must have equal length (up to 100 pairs)
• Returns results in input order: { latitude, longitude, elevation_m }
• Useful for geographic context, elevation-adjusted weather interpretation, or route planning

openmeteo_get_ensemble

Probabilistic ensemble weather forecast exposing all individual model member trajectories.

• Up to 16 forecast days (forecast_days 1–16, default 7) with optional past_days (0–92)
• Each requested variable is returned as per-member columns: temperature_2m_member01, temperature_2m_member02, … Use the spread across members to compute exceedance probabilities, interquantile ranges, and decision thresholds
• Available ensemble models: ecmwf_ifs025 (51 members, global 0.25°), gfs025 (31 members), icon_seamless (40 members, global/Europe blend), gem_global (21 members). Omit models to use the API default
• Response includes model (system used) and member_count (number of members)
• At least one of hourly_variables or daily_variables is required
• Large multi-member, multi-day pulls spill to DataCanvas when CANVAS_PROVIDER_TYPE=duckdb — output includes canvas_id and truncated: true; query with openmeteo_dataframe_query
• Configurable temperature, wind speed, and precipitation units

openmeteo_get_flood

GloFAS (Global Flood Awareness System) river discharge forecast and reanalysis via the Open-Meteo Flood API.

• Coordinate-based — no river ID needed; the API snaps to the nearest river grid point automatically
• Forecast horizon up to 210 days; reanalysis history from 1984-01-01 to present
• Use forecast_days for future outlook, start_date/end_date for historical analysis; both can be combined
• Available daily variables: river_discharge (ensemble mean), river_discharge_mean, river_discharge_min, river_discharge_max, river_discharge_median, river_discharge_p25 (25th percentile), river_discharge_p75 (75th percentile) — all in m³/s
• Returns null for coordinates outside GloFAS coverage (e.g., open ocean or areas without river network data)
• Discharge values reflect the GloFAS ensemble — percentile variables expose the uncertainty spread

Features

Built on @cyanheads/mcp-ts-core:

• Declarative tool definitions — single file per tool, framework handles registration and validation
• Unified error handling — handlers throw, framework catches, classifies, and formats
• Pluggable auth: none, jwt, oauth
• Swappable storage backends: in-memory, filesystem, Supabase, Cloudflare KV/R2/D1
• Structured logging with optional OpenTelemetry tracing
• STDIO and Streamable HTTP transports

Open-Meteo–specific:

• No API key required for non-commercial use — zero-config out of the box
• Self-contained geocoding: openmeteo_geocode resolves place names so agents don't need a separate geocoder
• ERA5 archive from 1940 to present with same variable schema as the forecast API — direct past/forecast comparisons on one schema
• Automatic columnar-to-record reshape: Open-Meteo returns parallel time/variable arrays; handlers convert to per-timestamp records with a *_units map
• DataCanvas spillover for openmeteo_get_historical and openmeteo_get_ensemble: large queries that exceed the inline record limit register a DuckDB dataframe for SQL querying
• Configurable base URLs for all seven API endpoints (forecast, archive, marine, air quality, geocoding, ensemble, flood) — override for testing or self-hosted deployments
• Attribution: Weather data by Open-Meteo.com (CC BY 4.0). Non-commercial use is free and keyless; commercial use requires Open-Meteo's paid API tier (~10,000 req/day, 5,000/hour fair-use ceiling for non-commercial)

Agent-friendly output:

• Geocode-first workflow: openmeteo_geocode returns the IANA timezone alongside coordinates — pass it directly as timezone to any weather tool
• Recovery hints on all error contracts — invalid variable names surface correction guidance with common variable examples
• Coordinate snapping transparency — responses echo the snapped latitude/longitude (Open-Meteo quantizes to the nearest model grid point) so agents can reason about grid alignment
• data_source: "CAMS" label on air quality results distinguishes modeled forecast data from measured station readings

Getting started

Public Hosted Instance

A public instance is available at https://open-meteo.caseyjhand.com/mcp — no installation required. Point any MCP client at it via Streamable HTTP:

{
  "mcpServers": {
    "open-meteo-mcp-server": {
      "type": "streamable-http",
      "url": "https://open-meteo.caseyjhand.com/mcp"
    }
  }
}

Self-Hosted / Local

Add the following to your MCP client configuration file.

{
  "mcpServers": {
    "open-meteo-mcp-server": {
      "type": "stdio",
      "command": "bunx",
      "args": ["@cyanheads/open-meteo-mcp-server@latest"],
      "env": {
        "MCP_TRANSPORT_TYPE": "stdio",
        "MCP_LOG_LEVEL": "info"
      }
    }
  }
}

Or with npx (no Bun required):

{
  "mcpServers": {
    "open-meteo-mcp-server": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@cyanheads/open-meteo-mcp-server@latest"],
      "env": {
        "MCP_TRANSPORT_TYPE": "stdio",
        "MCP_LOG_LEVEL": "info"
      }
    }
  }
}

Or with Docker:

{
  "mcpServers": {
    "open-meteo-mcp-server": {
      "type": "stdio",
      "command": "docker",
      "args": ["run", "-i", "--rm", "-e", "MCP_TRANSPORT_TYPE=stdio", "ghcr.io/cyanheads/open-meteo-mcp-server:latest"]
    }
  }
}

For Streamable HTTP, set the transport and start the server:

MCP_TRANSPORT_TYPE=http MCP_HTTP_PORT=3010 bun run start:http
# Server listens at http://localhost:3010/mcp

Prerequisites

• Bun v1.3.0 or higher (or Node.js v24+).
• No API key required. Non-commercial use is free and keyless.
• Commercial use requires Open-Meteo's paid API tier.

Installation

1. Clone the repository:

git clone https://github.com/cyanheads/open-meteo-mcp-server.git

2. Navigate into the directory:

cd open-meteo-mcp-server

3. Install dependencies:

bun install

Configuration

All configuration is validated at startup via Zod schemas. No API key is required for non-commercial use — all variables are optional.

See .env.example for the full list of optional overrides.

Running the server

Local development

• Build and run the production version:

  # One-time build
  bun run rebuild
  
  # Run the built server
  bun run start:http
  # or
  bun run start:stdio
  

• Run checks and tests:

  bun run devcheck  # Lint, format, typecheck, security
  bun run test      # Vitest test suite
  

Docker

docker build -t open-meteo-mcp-server .
docker run --rm -p 3010:3010 open-meteo-mcp-server

The Dockerfile defaults to HTTP transport, stateless session mode, and logs to /var/log/open-meteo-mcp-server. OpenTelemetry peer dependencies are installed by default — build with --build-arg OTEL_ENABLED=false to omit them.

Project structure

Development guide

See CLAUDE.md for development guidelines and architectural rules. The short version:

• Handlers throw, framework catches — no try/catch in tool logic
• Use ctx.log for request-scoped logging, ctx.state for tenant-scoped storage
• Register new tools in the tools[] array in src/index.ts
• Wrap external API calls: validate raw → normalize to domain type → return output schema; never fabricate missing fields

Contributing

Issues and pull requests are welcome. Run checks and tests before submitting:

bun run devcheck
bun run test

License

Apache-2.0 — see LICENSE for details.

Weather data by Open-Meteo.com — licensed CC BY 4.0.