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mcp server

open-meteo-mcp-server

Global weather via Open-Meteo: forecast, ERA5 archive, marine, air quality, geocoding, elevation.

Description as published by the maintainer. Source

  • version 0.3.4
  • active

active — Most recent push to the repository was 2026-07-31.

What this server can do

11 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.

openmeteo_dataframe_describe(canvas_id)
List the tables and their columns on a DataCanvas staged by openmeteo_get_forecast, openmeteo_get_historical, openmeteo_get_marine, openmeteo_get_air_quality, openmeteo_get_ensemble, openmeteo_get_flood, or openmeteo_get_climate. Call this first to discover table names before querying with openmeteo_dataframe_query. Required: canvas_id.
openmeteo_dataframe_query(sql, canvas_id)
Run a read-only SQL SELECT against tables staged on a DataCanvas by openmeteo_get_forecast, openmeteo_get_historical, openmeteo_get_marine, openmeteo_get_air_quality, openmeteo_get_ensemble, openmeteo_get_flood, or openmeteo_get_climate. Pass the canvas_id returned when any of those tools spills (truncated: true), and reference the exact table_name those tools return alongside it. Call openmeteo_dataframe_describe to list staged tables and their columns when you need to discover names. Required: canvas_id, sql.
openmeteo_get_air_quality(end_date, latitude, timezone, canvas_id, longitude, past_days, start_date, forecast_days, hourly_variables)
Modeled CAMS (Copernicus Atmosphere Monitoring Service) air quality: PM2.5, PM10, nitrogen dioxide, sulphur dioxide, ozone, carbon monoxide, dust, pollen, and European/US AQI indices. This is modeled grid data, not measured station readings — for measured data, use openaq-mcp-server. Forecast horizon up to 7 days, with optional past_days (up to 92) for recent history — or start_date and end_date together for an archive range, which returns real CAMS values back to at least 2022-10-01. One window per call: a date range is mutually exclusive with forecast_days and past_days, and needs both ends — a lone start_date or end_date is rejected. 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. A wide window — a large past_days or date range plus many variables — produces thousands of records; these spill to DataCanvas for SQL querying when canvas is enabled, and return a bounded preview with truncated: true when it is not. Required: latitude, longitude.
openmeteo_get_climate(models, end_date, latitude, timezone, canvas_id, longitude, start_date, daily_variables, wind_speed_unit, temperature_unit, precipitation_unit)
Long-range climate projections from bias-corrected daily CMIP6 models, covering 1950-01-01 to 2050-12-31 at any coordinate. Answers "what will conditions look like through 2050?" — the future-projection counterpart to openmeteo_get_historical (ERA5, what happened). Daily resolution only. Available models: CMCC_CM2_VHR4, FGOALS_f3_H, HiRAM_SIT_HR, MRI_AGCM3_2_S, EC_Earth3P_HR, MPI_ESM1_2_XR, NICAM16_8S. A model name outside that list is sent upstream rather than rejected here, so a model Open-Meteo adds later still works; if upstream rejects the request, the error names the offending model on its own rather than the whole requested list. With 2+ models each variable appears once per model with the model name as suffix (e.g. temperature_2m_max_CMCC_CM2_VHR4); a single or omitted model returns plain variable names. Not all models carry all variables — missing combinations return null. Multi-decade daily pulls across several models produce thousands of records and spill to DataCanvas for SQL querying when canvas is enabled, returning a bounded preview with truncated: true when it is not. Required: latitude, longitude, start_date, end_date.
openmeteo_get_elevation(latitudes, longitudes)
Terrain elevation from the Copernicus Digital Elevation Model (~90m resolution) for one or more coordinate pairs. Accepts up to 100 pairs per call. Useful for geographic context, elevation-adjusted weather interpretation, or route planning. Required: latitudes, longitudes.
openmeteo_get_ensemble(models, latitude, timezone, canvas_id, longitude, past_days, forecast_days, daily_variables, wind_speed_unit, hourly_variables, temperature_unit, precipitation_unit)
Probabilistic ensemble weather forecast — up to 64 ensemble members, up to 16 days ahead with optional past_days (0–92). Each member's values appear as separate columns named with a member suffix (e.g. temperature_2m_member01, temperature_2m_member02). Use the spread across members to compute exceedance probabilities, quantify forecast uncertainty, and build decision thresholds. Available models: ecmwf_ifs025_ensemble (51 members, global 0.25°), ecmwf_aifs025_ensemble (51, global 0.25°), ecmwf_ifs_europe_ensemble (51, Europe 9 km), ecmwf_aifs_europe_ensemble (51, Europe 31 km), google_weathernext2_ensemble (64, global 0.25°), ncep_gefs_seamless (31, global blend), ncep_gefs025 (31, global 0.25°), ncep_gefs05 (31, global 50 km, 35 days), ncep_aigefs025 (31, global 0.25°), icon_seamless_eps (20–40, global/Europe blend), icon_global_eps (40, global 26 km), icon_eu_eps (40, Europe 13 km), icon_d2_eps (20, Central Europe 2 km), gem_global_ensemble (21, global 0.25°), bom_access_global_ensemble (18, global 40 km), ukmo_global_ensemble_20km (18, global 20 km), ukmo_uk_ensemble_2km (3, UK 2 km), meteoswiss_icon_ch1_ensemble (11, Central Europe 1 km), meteoswiss_icon_ch2_ensemble (21, Central Europe 2 km). Omit models to use the API default blend. A regional model returns no data outside the area it covers; that comes back as an input error naming the coverage gap, not a transient failure, so pick a global model or move the coordinate inside the region rather than retrying. A model name this list does not carry is still sent upstream, so a newly added one keeps working. Large multi-member, multi-day pulls produce thousands of records and spill to DataCanvas when canvas is enabled, returning a bounded preview with truncated: true when it is not. At least one of hourly_variables or daily_variables is required. Required: latitude, longitude.
openmeteo_get_flood(end_date, latitude, timezone, canvas_id, longitude, start_date, forecast_days, daily_variables)
GloFAS (Global Flood Awareness System) river discharge forecast and historical reanalysis. Returns daily ensemble river discharge (m³/s) for the river nearest to the given coordinates — no river ID needed, the API snaps to the nearest stream. Forecast horizon up to 210 days ahead; reanalysis history back to 1984-01-01. One mode per call: forecast_days for the future outlook, or start_date and end_date together for reanalysis history. The two modes are mutually exclusive, and a date range needs both ends — a lone start_date or end_date is rejected. 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). Returns null for coordinates far from any river or in areas without GloFAS coverage. A wide reanalysis range produces thousands of daily records and spills to DataCanvas for SQL querying when canvas is enabled, returning a bounded preview with truncated: true when it is not. Required: latitude, longitude.
openmeteo_get_forecast(latitude, timezone, canvas_id, longitude, past_days, forecast_days, daily_variables, wind_speed_unit, hourly_variables, temperature_unit, precipitation_unit)
Weather forecast for coordinates: hourly and/or daily variables for up to 16 days ahead, with optional past_days (up to 92) for recent history. Use past_days instead of openmeteo_get_historical for dates within the last 1–5 days, since ERA5 has a variable lag. Returns per-timestamp records — each hourly entry contains a "time" field (ISO 8601) plus one key per requested variable; each daily entry contains a "time" field (YYYY-MM-DD) plus requested variables. 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. A wide window — a large past_days plus many hourly variables — produces thousands of records; these spill to DataCanvas for SQL querying when canvas is enabled, and return a bounded preview with truncated: true when it is not. At least one of hourly_variables or daily_variables is required. Required: latitude, longitude.
openmeteo_get_historical(end_date, latitude, timezone, canvas_id, longitude, start_date, daily_variables, wind_speed_unit, hourly_variables, temperature_unit, precipitation_unit)
Historical weather from the ERA5 reanalysis archive (1940–present). Requires start_date and end_date (ISO 8601 date, e.g., "2024-07-01"). ERA5 has a variable lag of up to ~5 days — for dates within the last week, use openmeteo_get_forecast with past_days instead. Uses the same variable names as the forecast API for direct comparison. Large date ranges (multi-year hourly) produce thousands of records — these spill to DataCanvas for SQL querying when canvas is enabled, and return a bounded preview with truncated: true when it is not. At least one of hourly_variables or daily_variables is required. Required: latitude, longitude, start_date, end_date.
openmeteo_get_marine(end_date, latitude, timezone, canvas_id, longitude, past_days, start_date, forecast_days, daily_variables, hourly_variables)
Marine wave and ocean conditions for a coastal or ocean coordinate: wave height, wave period, wave direction, wind-wave height, swell height, sea-surface temperature. Forecast horizon up to 8 days, with optional past_days (up to 92) for recent history — or start_date and end_date together for an archive range, which returns real wave values back to at least 2022. One window per call: a date range is mutually exclusive with forecast_days and past_days, and needs both ends — a lone start_date or end_date is rejected. Returns per-timestamp records — each entry contains a "time" field plus one key per requested variable. Best for open-ocean and coastal exposed points — sheltered inland waters return near-zero wave values. 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: wave_height_max, wave_direction_dominant, wave_period_max. Note: ocean_current_velocity is null for non-open-ocean coordinates. A wide window — a large past_days or date range plus many variables — produces thousands of records; these spill to DataCanvas for SQL querying when canvas is enabled, and return a bounded preview with truncated: true when it is not. Required: latitude, longitude.
openmeteo_search_locations(name, count, country, language)
Resolve a place name to ranked coordinate matches with country, region, elevation, timezone, and population. Required prerequisite for name-based queries — all weather tools take latitude/longitude, not place names. Search by a bare place name (city, region, or landmark); never fold a qualifier into it — pass "Baoding", not "Baoding Hebei", and "Paris", not "Paris, France". To disambiguate places that share a name, set the country input (ISO 3166-1 alpha-2, e.g. "US") and/or read the admin1 and country fields on each ranked result — admin1 is a result field for choosing among matches, not a search input. Returns up to 10 matches ranked by population/relevance. Required: name.

Last successful function declaration observed on . Source: https://open-meteo.caseyjhand.com/mcp. We list what the server declared; we do not call any of these functions.

Endpoint status observed on . Source: https://open-meteo.caseyjhand.com/mcp.

Signals

These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.

Signal Value What it measures Window Observed Source
GitHub stars 2 Number of GitHub accounts that bookmarked this repository since it was created. It is a bookmark count, not installs, not active users and not quality. cumulative, all time GitHub
Last commit 2026-07-31 Date of the most recent push to any branch. This is the strongest cheap indicator of whether the project is still maintained. point in time GitHub
Open issues 0 Open issues plus open pull requests, as GitHub counts them together. A high number can mean an active project or an abandoned one. as of fetch GitHub
Latest published version 0.3.4 Latest version string the maintainer published to the registry. as of fetch Model Context Protocol
Registry record last updated 2026-07-31 When the registry record was last updated by its maintainer. point in time Model Context Protocol
License Apache-2.0 Licence GitHub detected in the repository. Detection can be wrong; the LICENSE file is authoritative. as of fetch GitHub
First listed in the MCP Registry 2026-07-31 Date this server was first published to the official MCP Registry. Not a usage or quality measure. point in time Model Context Protocol
repository status active The repository exists on GitHub and is not archived. This says nothing about how recently it was worked on. as of fetch GitHub
mcp tools declared 11 tools Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. as of probe open-meteo.caseyjhand.com
mcp endpoint status ok The server listed 11 functions when asked. as of probe open-meteo.caseyjhand.com

Where to get it

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These share tags the maintainers applied themselves, such as forecast, open-meteo, weather, cyanheads. Common tags like "mcp" or "ai" are ignored for this: agreeing with six hundred other projects is not a similarity.

This is not a recommendation and not a test result. It is a map of what the authors said their work is about.

Also from cyanheads

How the author describes it

Topics the maintainer set on GitHub: ai-agents, ai-tools, climate, cyanheads, forecast, mcp, mcp-server, model-context-protocol, open-meteo, typescript, weather.

This record as data

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Sources

  1. cyanheads/open-meteo-mcp-server on GitHub — GitHub, observed , trust tier 3.
  2. Tools declared by the MCP server at https://open-meteo.caseyjhand.com/mcp — open-meteo.caseyjhand.com, observed , trust tier 4.
  3. Official MCP Registry — Model Context Protocol, observed , trust tier 1.