Open Meteo MCP Server

@cyanheads/open-meteo-mcp-server Geocode places, fetch global weather forecasts, historical climate, marine conditions, air quality, and terrain elevation via MCP. STDIO or Streamable HTTP. 12 Tools

Public Hosted Server: https://open-meteo.caseyjhand.com/mcp

Overview

Global weather from Open-Meteo: forecasts, history back to 1940, marine conditions, air quality, probabilistic ensembles, river discharge, and climate projections. Look up any place by name, pull hourly and daily variables, and run SQL over large results. No API key required.

Tools

The server also publishes an agent skill, open-meteo-guide (skill://open-meteo-guide/SKILL.md, source in skills/open-meteo-guide/): which tool answers which question, plus the gates for exceedance odds, frost, comparisons to a normal, climate shifts, surf spots, river cells, and AQI timing.

Capability reference

openmeteo_search_locations tool

• A bare place name ("Paris", not "Paris, France"), with optional country (ISO 3166-1 alpha-2) and language (default en); count 1–10, default 5
• Each result carries coordinates, an IANA timezone, country / country_code, admin1 / admin2, population, and feature_code; no match fails as no_results
• A notice flags a top match with null or sub-100,000 population, which often means a historic exonym ("Bangalore", "Calcutta")
• A native-script query that misses under en is retried once in the language its script implies; effective_language names it when the retry succeeds (上海 → zh), and results come back localized

openmeteo_get_forecast tool

• At least one of current_variables, hourly_variables, or daily_variables (up to 50 each); forecast_days 1–16 (default 7), past_days 0–92 (default 0)
• Per-timestamp hourly / daily records with hourly_units / daily_units; current_variables adds a current object from 15-minute data (interval 900) plus current_units
• Omitting models reads Open-Meteo best_match; models compares deterministic models (e.g. ncep_gfs_seamless, ecmwf_ifs025, dwd_icon_seamless; older spellings like gfs_seamless still resolve). With 2+ models each hourly and daily column carries a model-name suffix, and models is echoed. current_variables takes at most one model (current_multi_model)
• A regional model with no data at the point is dropped from a multi-model response: the notice names it, or, when one model is left with unsuffixed columns, says the values cannot be attributed to a model; when every requested model misses, the call fails as coverage_gap
• Prefer past_days over openmeteo_get_historical for the last week, where the archive's ERA5 components lag

openmeteo_get_historical tool

• start_date and end_date required (YYYY-MM-DD, from 1940-01-01), plus at least one of hourly_variables / daily_variables, named as on the forecast tool
• Omitting models reads Best Match (IFS HRES + ERA5 + ERA5-Land, source varies by date); up to 8 models pin a source. The ERA5 family runs about a week behind, ecmwf_ifs has no delay, and cerra covers Europe only
• Output echoes models and the returned date_range; with 2+ models each column carries a model-name suffix. A model with no data at the point (cerra outside Europe) is dropped from a multi-model response; the notice names it, or, when one model is left with unsuffixed columns (era5,cerra), says the values cannot be attributed to a model

openmeteo_get_marine tool

• One window per call: forecast_days 1–8 (upstream default 7) with past_days 0–92, or a start_date / end_date archive range back to at least 2022. Mixing them fails as forecast_window_conflict, a lone date as date_range_incomplete
• At least one of current_variables, hourly_variables, or daily_variables (wave height, period, and direction, swell, sea-surface temperature); sheltered water returns near-zero waves, a point well inland returns nulls (named in the notice), a land point near the coast is answered from a nearby sea cell with real values and no notice (check the response's latitude / longitude is the water you meant), and ocean_current_velocity is null off the open ocean
• current_variables takes the hourly names and adds a current object from 15-minute data (interval 900) plus current_units, alongside either window; a daily name there fails as variable_wrong_cadence
• length_unit (metric / imperial) sets every height, sea_level_height_msl included, in m or ft; wind_speed_unit (kmh, mph, ms, kn) sets ocean_current_velocity; temperature_unit (celsius / fahrenheit) sets sea_surface_temperature. Open-Meteo converts the values, and the *_units maps label them (mph reads mp/h)

openmeteo_get_air_quality tool

• One window per call: forecast_days 1–7 (upstream default 5) with past_days 0–92, or a start_date / end_date archive range from August 2022 (us_aqi starts a day later). Same forecast_window_conflict and date_range_incomplete rejections as the marine tool
• At least one of current_variables (a current object, interval 3600) or hourly_variables: PM2.5, PM10, NO2, SO2, O3, CO, dust, pollen, European and US AQI. Pollen is modeled for Europe only, in pollen season; elsewhere it comes back null and the notice names it
• data_source: "CAMS" marks every response as modeled grid data, not station measurements

openmeteo_get_elevation tool

• Parallel latitudes[] / longitudes[] arrays of up to 1,000 pairs; unequal lengths fail as coordinate_count_mismatch, compared on the full arrays
• Samples the first 100 pairs (the Elevation API's per-request limit) in one upstream request; unprocessed_count says how many were left and next_offset is where to resume — resubmit latitudes.slice(next_offset) and longitudes.slice(next_offset) until next_offset is null. A call of 100 or fewer returns 0 / null. Each sampled pair counts as one call against Open-Meteo's per-IP limits (600 a minute), so a seventh 100-pair slice inside one minute comes back rate_limited
• elevations[] in input order, each { latitude, longitude, elevation_m } from the Copernicus DEM (~90 m); elevation_m is null where the DEM has no value (latitude 90), without failing the rest of the call

openmeteo_get_ensemble tool

• At least one of hourly_variables / daily_variables; forecast_days 1–35 (default 7), past_days 0–92. Most models end within 16 days (ncep_gefs05 runs to about day 34); past that, columns are null and the notice says where data ends
• models takes one ensemble name (e.g. ecmwf_ifs025_ensemble, 51 members; gem_global_ensemble, 21); omitted or blank selects ecmwf_ifs025_ensemble, echoed in model
• Each variable returns per-member columns (temperature_2m_member01, …) across up to 64 members; member_count excludes the control run. A regional model queried outside its coverage fails as a non-retryable input error; switch to a global model

openmeteo_get_flood tool

• daily_variables required (up to 20), all in m³/s: river_discharge plus the statistics river_discharge_mean / _median / _min / _max / _p25 / _p75, which carry ensemble spread on forecast-run days and repeat river_discharge on reanalysis days
• One mode per call: forecast_days 1–210 (the current GloFAS run reaches about six months; later days are null), or a start_date / end_date range from 1984-01-01. Mixing them fails as forecast_days_conflict, a lone date as date_range_incomplete
• Omitting models reads seamless_v4: consolidated_v4 reanalysis before 2023-08-05, forecast_v4 runs from then on. models: ["consolidated_v4"] reads the reanalysis through 2025-05-31 (null after, and on forecast_days); forecast_v4 starts 2023-06-01. Output echoes models; with 2+ models each column carries a model-name suffix, and the notice names null spans
• Discharge comes from the GloFAS 0.05° grid cell nearest the coordinate, not reliably the cell on the river you mean; when the value is far below what the river carries, step the coordinate 0.05° across the channel and compare. Null outside GloFAS coverage

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