Open Meteo Mcp Server

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.

Terrain elevation from the Copernicus Digital Elevation Model (~90m resolution) for one or more coordinate pairs. Accepts up to 1,000 pairs and samples the first 100 in one upstream request; when more were submitted, unprocessed_count says how many remain and next_offset is where to resume — resubmit latitudes.slice(next_offset) and longitudes.slice(next_offset) of the arrays just sent, until next_offset is null. Open-Meteo counts each sampled pair as one call against its per-IP limit of 600 a minute, so a seventh full slice inside one minute comes back rate_limited — space later slices about a minute apart. Useful for geographic context, elevation-adjusted weather interpretation, or route planning. A point the DEM has no value for (latitude 90) returns elevation_m null; the other points in the call are unaffected.

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 week, since the archive’s ERA5 components lag by about a week. 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. Set current_variables for conditions at this instant — Open-Meteo serves those from 15-minute model data, which is more precise than picking the nearest hourly row, and the response carries a current object plus a current_units map. Set models to compare deterministic models side by side (e.g., ["ncep_gfs_seamless", "ecmwf_ifs025", "dwd_icon_seamless"]) — where they disagree, the forecast is uncertain. With 2+ models every hourly and daily key carries the model name as a suffix (temperature_2m_ecmwf_ifs025); with models omitted the data is Open-Meteo’s best_match blend. A wide window — a large past_days plus many hourly variables — produces thousands of records; these spill to a DataCanvas when canvas is enabled, returning canvas_id and table_name with truncated: true — inspect the staged columns with openmeteo_dataframe_describe, then query the full set with openmeteo_dataframe_query. With canvas disabled they return a bounded preview instead. At least one of current_variables, hourly_variables, or daily_variables is required.

Historical weather from the Open-Meteo reanalysis archive (1940–present). Requires start_date and end_date (ISO 8601 date, e.g., "2024-07-01"). With models omitted the archive answers from Best Match, which blends IFS HRES, ERA5, and ERA5-Land seamlessly — so the source varies by date and no single update lag describes the response. Set models to pin a consistent source for a multi-decade series: the ERA5 family updates daily with about a week of delay, while IFS HRES has none, so for the last week either request models: ["ecmwf_ifs"] or use openmeteo_get_forecast with past_days. Available models: best_match (default, blends IFS HRES + ERA5 + ERA5-Land), ecmwf_ifs (global 9 km, updated every 6 hours, no delay), ecmwf_ifs_analysis_long_window (global 9 km, daily, 2 days delay), era5_seamless (ERA5 and ERA5-Land combined), era5 (global 0.25° (~25 km), daily, about a week delay), era5_land (global 0.1° (~11 km), daily, about a week delay), era5_ensemble (global 0.5° (~55 km), daily, about a week delay), cerra (Europe only, 5 km, no real-time updates). 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 a DataCanvas when canvas is enabled, returning canvas_id and table_name with truncated: true; inspect the staged columns with openmeteo_dataframe_describe, then query the full set with openmeteo_dataframe_query. With canvas disabled they return a bounded preview instead. At least one of hourly_variables or daily_variables is required.

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 water such as a sound or bay returns near-zero wave values, and a land point well inland returns null values, which the notice reports. A land point near the coast is answered from a nearby sea cell with real values and no notice, so check the response latitude and longitude is the water you meant. 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. Set current_variables for conditions at this instant — Open-Meteo serves those from 15-minute model data, and the response carries a current object plus a current_units map, alongside either window. length_unit "imperial" returns heights in ft, wind_speed_unit sets the ocean_current_velocity unit, and temperature_unit the sea_surface_temperature unit — converted upstream, with every unit read from the *_units maps. A wide window — a large past_days or date range plus many variables — produces thousands of records; these spill to a DataCanvas when canvas is enabled, returning canvas_id and table_name with truncated: true — inspect the staged columns with openmeteo_dataframe_describe

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; the CAMS global archive begins in August 2022, and earlier dates return rows of nulls. 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. Pollen is modeled for Europe only, and only in pollen season — elsewhere the pollen values come back null, and the notice names them. Set current_variables for pollutant and AQI values at this instant — returned as a current object plus a current_units map, and enough on its own without hourly_variables; the block’s interval field is its aggregation interval in seconds (3600 on this endpoint). A wide window — a large past_days or date range plus many variables — produces thousands of records; these spill to a DataCanvas when canvas is enabled, returning canvas_id and table_name with truncated: true — inspect the staged columns with openmeteo_dataframe_describe, then query the full set with openmeteo_dataframe_query. With canvas disabled they return a bounded preview instead.

Probabilistic ensemble weather forecast — up to 64 ensemble members, up to 35 days ahead with optional past_days (0–92). Most models end within 16 days; a model's columns are null past its horizon, and the response notice names where its data ends. 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 ecmwf_ifs025_ensemble. 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 a DataCanvas when canvas is enabled, returning canvas_id and table_name with truncated: true — inspect the staged columns with openmeteo_dataframe_describe, then query the full set with openmeteo_dataframe_

GloFAS (Global Flood Awareness System) daily river discharge (m³/s) from the GloFAS 0.05° grid cell nearest the given coordinates — no river ID needed. The response's latitude and longitude name that cell. It is not reliably on the river intended: a point near a confluence, a pair of parallel channels, or a bank can resolve to a cell with little flow. When the value is far below what the named river carries, step the coordinate 0.05° across the channel and compare the values. One mode per call: forecast_days for the outlook, or start_date and end_date together for a date range from 1984-01-01. The two modes are mutually exclusive, and a date range needs both ends — a lone start_date or end_date is rejected. forecast_days accepts up to 210, but the current GloFAS run sets how far the data reaches (about six months ahead); later days come back null and the notice names where the data ends. What a date range holds depends on the model. Omitting models reads seamless_v4: consolidated_v4 reanalysis before 2023-08-05, then forecast-run discharge from 2023-08-05 on, so a range crossing that date changes source mid-series. Set models to pin a source: seamless_v4 (default — consolidated_v4 reanalysis before 2023-08-05, forecast_v4 runs from 2023-08-05 into the live forecast), consolidated_v4 (reanalysis from 1984 through 2025-05-31; null after it and on every forecast_days call), forecast_v4 (forecast runs from 2023-06-01 into the live forecast; null before). consolidated_v4 is the only way to read reanalysis for 2023-08-05 through 2025-05-31. With 2+ models every daily column is suffixed with the model name (river_discharge_consolidated_v4). Available daily variables: "river_discharge" (the daily discharge value; the mean across ensemble members is "river_discharge_mean"), and the ensemble statistics "river_discharge_mean", "river_discharge_median", "river_discharge_min", "river_discharge_max", "river_discharge_p25" (25th percentile), and "river_discharge_p75" (75th percent

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 (the observed archive, 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 a DataCanvas when canvas is enabled, returning canvas_id and table_name with truncated: true — inspect the staged columns with openmeteo_dataframe_describe, then query the full set with openmeteo_dataframe_query. With canvas disabled they return a bounded preview instead.

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. Returns up to 100 leading rows — fewer when wide rows would pass the 80,000-character inline ceiling — plus row_count — exact, or a lower bound flagged by count_error when counting fails or runs past 5 seconds; page the rest with LIMIT / OFFSET.

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.