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open-meteo-mcp-server

by io.github.cyanheads

Openmeteo Get Climate

openmeteo_get_climate
Read-onlyIdempotent

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsNoCMIP6 models to include: CMCC_CM2_VHR4, FGOALS_f3_H, HiRAM_SIT_HR, MRI_AGCM3_2_S, EC_Earth3P_HR, MPI_ESM1_2_XR, NICAM16_8S. With 2+ models each variable column is suffixed with the model name (e.g. temperature_2m_max_MRI_AGCM3_2_S). Omit to use the API default (a single model, unsuffixed columns). A name outside this list is sent upstream rather than rejected here.
end_dateYesEnd date (YYYY-MM-DD, inclusive, max 2050-12-31). Must be on or after start_date.
latitudeYesLatitude in decimal degrees. Use openmeteo_search_locations to resolve a place name to coordinates.
timezoneNoIANA timezone or "auto". Default "auto".auto
canvas_idNoDataCanvas token for multi-decade or multi-model queries. When a result is too large to return inline — driven by total payload size, so a wide multi-model pull can spill at any row count — it spills to this canvas for SQL querying. Omit to create a fresh canvas.
longitudeYesLongitude in decimal degrees.
start_dateYesStart date (YYYY-MM-DD, e.g., "2049-01-01"). CMIP6 projections cover 1950-01-01 to 2050-12-31.
daily_variablesNoDaily climate variables to fetch (e.g., ["temperature_2m_max", "temperature_2m_min", "precipitation_sum", "wind_speed_10m_mean", "shortwave_radiation_sum"]). Required — the Climate API is daily-only.
wind_speed_unitNoWind speed unit. Default "kmh".kmh
temperature_unitNoTemperature unit. Default "celsius".celsius
precipitation_unitNoPrecipitation unit. Default "mm".mm

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dailyNoPer-day records with "time" (YYYY-MM-DD) + one key per requested variable — per-model suffixed keys when 2+ models were requested (e.g. temperature_2m_max_CMCC_CM2_VHR4). Null values mean the model does not carry that variable. When truncated, contains only a preview — query canvas_id for the full dataset when one is present.
errorNoPresent when the call failed. Absent on success.
modelsNoClimate models requested — echoes the models parameter. Absent when models was omitted (API default model; the response carries no provenance).
noticeNoWarning that a requested variable came back with no data — names each column whose unit is "undefined", which is how the endpoint reports a name it parsed but does not serve.
latitudeNoSnapped latitude (Open-Meteo snaps to nearest grid point)
timezoneNoResolved IANA timezone
canvas_idNoDataCanvas token for the staged full dataset. Present only when truncated is true AND DataCanvas is enabled (CANVAS_PROVIDER_TYPE=duckdb) — absent otherwise, in which case the preview is all this response carries. Query with SQL using this token.
elevationNoElevation at grid point (meters)
longitudeNoSnapped longitude
truncatedNoTrue when the response was too large to return inline, so daily carries a bounded preview rather than the full set. With DataCanvas enabled the complete data is staged at canvas_id. With it disabled there is no canvas_id, and the omitted rows are reached only by narrowing the request.
date_rangeNoDate range of returned data
table_nameNoDuckDB table name for the staged data — pass to openmeteo_dataframe_query. Present only alongside canvas_id.
daily_unitsNoColumn → unit string for daily data (e.g., {"temperature_2m_max_CMCC_CM2_VHR4": "°C"}).
record_countNoTotal number of daily records — the full upstream total when truncated is true, not the length of the daily preview.

TDQS

A4.4/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With readOnlyHint and idempotentHint already declared, the description goes further: it discloses that unknown model names are sent upstream rather than rejected, error messages name the offending model individually, variable naming depends on model count, missing model-variable combos return null, and large results spill to DataCanvas with a truncated flag. These are valuable behavioral facts beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but front-loaded with the core purpose and structured logically: scope, contrast, resolution, model list, edge cases, and output behavior. Every sentence adds value; it's dense but not redundant. A slight trim could improve readability, but it's efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the 11 parameters, output schema, and sibling tools, the description covers essential operational details: model validation behavior, variable naming, missing data handling, and canvas spill with truncation. It leaves little an agent needs to infer, making it complete for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds some contextual notes (e.g., daily resolution requires daily_variables, model suffix behavior) that are also largely present in the schema itself. It doesn't add significant new parameter-level meaning beyond the schema, so a 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb, resource, and scope: 'Long-range climate projections from bias-corrected daily CMIP6 models... at any coordinate.' It explicitly distinguishes from openmeteo_get_historical as the future-projection counterpart, making it clear what this tool does and how it differs from siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It clearly positions the tool as the future counterpart to openmeteo_get_historical, giving a strong when-to-use cue. It also mentions 'Daily resolution only' as a constraint. However, it doesn't explicitly mention when not to use other siblings like get_forecast or get_ensemble, leaving some ambiguity; still, the primary usage context is well covered.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.5/5.0
Disambiguation4/5

Each weather data tool (forecast, historical, marine, air_quality, ensemble, flood, climate) targets a distinct domain and data source, so they are largely unambiguous. However, openmeteo_get_forecast with past_days and openmeteo_get_historical overlap for recent dates, though the descriptions explicitly clarify which to use when, slightly muddying the boundary.

Naming Consistency5/5

Tool names follow a consistent openmeteo_<verb>_<object> pattern with clear actions (get, search, describe, query) and objects (forecast, historical, marine, air_quality, ensemble, flood, climate, elevation, locations, dataframe). This is highly predictable and uniform across all 11 tools.

Tool Count5/5

With 11 tools, the server covers a comprehensive set of weather data categories (forecast, historical, marine, air quality, ensemble, flood, climate, elevation) plus location search and dataframe utilities. This is well-scoped for a data-heavy weather API without being bloated, and each tool serves a distinct purpose.

Completeness5/5

The tool surface covers all major weather data needs: forecasts, historical reanalysis, marine conditions, air quality, ensemble forecasts, flood discharge, climate projections, and elevation. Location search is a proper prerequisite for coordinate-based queries, and dataframe query/describe handle large result sets, filling any gaps for data analysis workflows.

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