# io.github.cyanheads/open-meteo-mcp-server

> Global weather via Open-Meteo: forecast, historical, marine, air quality, geocoding, elevation.

- name: io.github.cyanheads/open-meteo-mcp-server
- version: 0.4.1
- connection class: R0 (autonomous)
- trust: 94/100
- quality: 78/100 (good)
- owner: not claimed
- descriptor: https://api.protogrid.dev/v1/servers/io.github.cyanheads%2Fopen-meteo-mcp-server
- tools: https://api.protogrid.dev/v1/servers/io.github.cyanheads%2Fopen-meteo-mcp-server/tools

**An agent can connect right now, no human step.** Remote endpoint, no authentication, reachable on the last probe.

## Connection (mcpServers)

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


## Trust

- hygiene: 100
- liveness: 100
- freshness: 100
- provenance: 80
- drivers: +repository +namespace-matches-repo +reachable +uptime-100% +updated-7d-ago
- Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed.

## Quality 78/100 (good)

### protocol: 88 (weight 25)

- pass `protocol.modern`: supports 2026-07-28 (server/discover)
- pass `protocol.stateless`: answers without a session
- pass `protocol.transport`: streamable HTTP
- warn `protocol.list_ttl`: tools/list declares ttlMs 0, so clients re-fetch it on every use

### authorization: n/a (weight 15)

- n/a `auth.prm`, `auth.as_metadata`, `auth.cimd`: no remote uses OAuth
- n/a `auth.secret_in_url`: no templated URL

### tool hygiene: 71 (weight 30)

- pass `tools.descriptions`: every tool has a description
- warn `tools.description_length`: 7 tools have descriptions over 1,024 characters, which crowds the context
- pass `tools.schemas`: every tool has an object input schema
- pass `tools.annotations`: 11 of 11 tools declare readOnlyHint or destructiveHint
- fail `tools.directory_hints`: no tool declares all four of readOnlyHint, destructiveHint, idempotentHint and openWorldHint; missing: openmeteo_search_locations (destructiveHint, openWorldHint); openmeteo_get_elevation (destructiveHint, openWorldHint); openmeteo_get_forecast (destructiveHint, openWorldHint); …
- warn `tools.token_cost`: about 10,788 tokens to load every tool
- pass `tools.api_dump`: tools are not a one-to-one API dump

### stability: 75 (weight 15)

- warn `stability.changes`: 20 tool changes in 30 days
- pass `stability.rug_pull`: no tool changed its meaning under the same name

### dependencies: n/a (weight 15)

- n/a `deps.known_vulns`, `deps.mcp_sdk_version`, `deps.resolvable`: deps.dev does not know @cyanheads/open-meteo-mcp-server@0.4.1, @cyanheads/open-meteo-mcp-server@0.4.1

- tools: 11, about 10,788 tokens to load them all
- tool set hash: 33ef4011f064b8f86a74171c99434dc3
- tools last changed: 2026-10-13T04:13:42.411Z
- badge: [![protogrid quality](https://protogrid.dev/badge/io.github.cyanheads/open-meteo-mcp-server.svg)](https://protogrid.dev/servers/io.github.cyanheads/open-meteo-mcp-server)
- Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed.

### Tool changes

- 2026-10-13 `openmeteo_dataframe_query` changed (description, outputSchema)
  - before: 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.
  - after: 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.
- 2026-10-13 `openmeteo_get_climate` changed (inputSchema, outputSchema)
- 2026-10-13 `openmeteo_get_flood` changed (description, inputSchema, outputSchema)
  - before: GloFAS (Global Flood Awareness System) river discharge forecast and historical reanalysis. Returns daily ensemble river discharge (m³/s) for the largest modeled river within 5 km of the given coordinates — no river ID needed. That river is not always the closest one: at 5 km resolution a point near a confluence or a pair of parallel channels can resolve to an unintended reach. When the returned discharge looks unrepresentative for the intended river, Open-Meteo suggests varying the coordinate by about 0.1° and comparing the values. 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 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 it returns a bounded preview instead.
  - after: GloFAS (Global Flood Awareness System) river discharge forecast and historical reanalysis. Returns daily river discharge (m³/s) for the largest modeled river within 5 km of the given coordinates — no river ID needed. That river is not always the closest one: at 5 km resolution a point near a confluence or a pair of parallel channels can resolve to an unintended reach. When the returned discharge looks unrepresentative for the intended river, Open-Meteo suggests varying the coordinate by about 0.1° and comparing the values. 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" (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 percentile). The statistics are computed across ensemble members in forecast mode only; in reanalysis mode they repeat river_discharge and are not uncertainty estimates. 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 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 it returns a bounded preview instead.
- 2026-10-13 `openmeteo_get_ensemble` changed (description, inputSchema, outputSchema)
  - before: 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 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.
  - after: 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_query. With canvas disabled they return a bounded preview instead. At least one of hourly_variables or daily_variables is required.
- 2026-10-13 `openmeteo_get_air_quality` changed (description, inputSchema, outputSchema)
  - before: 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. 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 reports how often that value updates (3600 seconds 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.
  - after: 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.
- 2026-10-13 `openmeteo_get_marine` changed (description, inputSchema, outputSchema)
  - before: 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 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.
  - after: 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 returns null values, which the notice reports. 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 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.
- 2026-10-13 `openmeteo_get_historical` changed (inputSchema, outputSchema)
- 2026-10-13 `openmeteo_get_forecast` changed (outputSchema)
- 2026-10-13 `openmeteo_get_elevation` changed (description, outputSchema)
  - before: 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.
  - after: 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. A point the DEM has no value for (latitude 90) returns elevation_m null; the other points in the call are unaffected.
- 2026-10-13 `openmeteo_search_locations` changed (inputSchema, outputSchema)

Full history: https://api.protogrid.dev/v1/servers/io.github.cyanheads%2Fopen-meteo-mcp-server/changes

## Dependencies

- npm @cyanheads/open-meteo-mcp-server@0.4.1: unknown
- npm @cyanheads/open-meteo-mcp-server@0.4.1: unknown

Dependency graphs from deps.dev; advisories from OSV.dev, including the GitHub Advisory Database and the PyPI Advisory Database; all CC BY 4.0.

## Remotes

- https://open-meteo.caseyjhand.com/mcp (streamable-http, auth none, reachable true, uptime30d 1)

## Packages

- npm @cyanheads/open-meteo-mcp-server@0.4.1
- npm @cyanheads/open-meteo-mcp-server@0.4.1

## Tools (11)

- `openmeteo_dataframe_describe`: 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.
- `openmeteo_dataframe_query`: 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.
- `openmeteo_get_air_quality`: 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.
- `openmeteo_get_climate`: 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.
- `openmeteo_get_elevation`: 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. A point the DEM has no value for (latitude 90) returns elevation_m null; the other points in the call are unaffected.
- `openmeteo_get_ensemble`: 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_query. With canvas disabled they return a bounded preview instead. At least one of hourly_variables or daily_variables is required.
- `openmeteo_get_flood`: GloFAS (Global Flood Awareness System) river discharge forecast and historical reanalysis. Returns daily river discharge (m³/s) for the largest modeled river within 5 km of the given coordinates — no river ID needed. That river is not always the closest one: at 5 km resolution a point near a confluence or a pair of parallel channels can resolve to an unintended reach. When the returned discharge looks unrepresentative for the intended river, Open-Meteo suggests varying the coordinate by about 0.1° and comparing the values. 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" (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 percentile). The statistics are computed across ensemble members in forecast mode only; in reanalysis mode they repeat river_discharge and are not uncertainty estimates. 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 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 it returns a bounded preview instead.
- `openmeteo_get_forecast`: 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 the archive’s ERA5 components lag by up to ~5 days. 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. 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.
- `openmeteo_get_historical`: 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 5-day delay, while IFS HRES has none, so for the last few days 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, 5 days delay), era5_land (global 0.1° (~11 km), daily, 5 days delay), era5_ensemble (global 0.5° (~55 km), daily, 5 days 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.
- `openmeteo_get_marine`: 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 returns null values, which the notice reports. 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 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.
- `openmeteo_search_locations`: 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.