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open-meteo-mcp-server

by io.github.cyanheads

Openmeteo Get Historical

openmeteo_get_historical
Read-onlyIdempotent

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
end_dateYesEnd date (YYYY-MM-DD, inclusive). Must be on or after start_date. For dates within the last ~5 days, use openmeteo_get_forecast with past_days instead.
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-year or multi-variable queries. When a result is too large to return inline — driven by total payload size, so a wide multi-variable 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., "2024-07-01"). ERA5 covers from 1940-01-01 to approximately 5 days ago.
daily_variablesNoDaily summary variables (e.g., ["temperature_2m_max", "temperature_2m_min", "precipitation_sum", "wind_speed_10m_max"]). Daily names only — an hourly name such as cloud_cover or temperature_2m belongs in hourly_variables and is rejected here; for a daily summary of an hourly variable use its published aggregate (cloud_cover_max, cloud_cover_mean, cloud_cover_min). At least one of hourly_variables or daily_variables required.
wind_speed_unitNoWind speed unit. Default "kmh".kmh
hourly_variablesNoHourly ERA5 variables (e.g., ["temperature_2m", "precipitation", "wind_speed_10m", "relative_humidity_2m", "cloud_cover", "soil_moisture_0_to_7cm"]). Hourly names only — a daily aggregate such as temperature_2m_max or precipitation_sum belongs in daily_variables and is rejected here. At least one of hourly_variables or daily_variables required.
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) + variable keys. Absent when only hourly_variables were requested. 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.
hourlyNoPer-hour records with "time" (ISO 8601) + variable keys. Absent when only daily_variables were requested. When truncated, contains only a preview — query canvas_id for the full dataset when one is present.
noticeNoWarning that a requested variable came back with no data — names each column whose unit is "undefined", which is how the archive reports a name it parsed but does not serve in the requested cadence.
latitudeNoSnapped latitude
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 hourly and daily carry a bounded preview rather than the full set. With DataCanvas enabled the complete data is staged at canvas_id — every hourly and daily row, including any column the preview omits. 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_unitsNoVariable → unit string for daily data. Absent when no daily_variables were requested.
hourly_unitsNoVariable → unit string for hourly data (e.g., {"temperature_2m": "°C", "precipitation": "mm"}). Absent when no hourly_variables were requested.
record_countNoTotal number of records (hourly + daily rows) — the full upstream total when truncated is true, not the combined length of the hourly and daily previews.

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds valuable behavioral context: the ERA5 variable lag of ~5 days, the spill-to-canvas behavior for large results with truncated: true when canvas is disabled, and the bounded preview behavior. This goes beyond the annotations, though it doesn't detail the exact return structure (but an output schema exists).

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

Conciseness5/5

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

The description is a single, dense paragraph that front-loads the core purpose and date range, then covers the lag caveat, variable compatibility, and large-result behavior. Every sentence adds new information with no redundancy, and it's appropriately sized for an 11-parameter tool.

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 tool's complexity (11 params, 4 required, output schema present), the description is complete. It covers the data source, date constraints, the alternative tool for recent dates, variable naming conventions, the required parameter condition, and the large-result spill behavior. The output schema handles return values, so nothing critical is missing for an agent to call this correctly.

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

Parameters4/5

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

Schema description coverage is 100%, so the schema already documents all 11 parameters thoroughly. The description adds value by explaining the relationship between hourly and daily variables (e.g., daily aggregates like temperature_2m_max belong in daily_variables, not hourly), and the spill-to-canvas behavior tied to canvas_id. This complements the schema rather than repeating it, earning a score above the baseline 3.

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 clearly states the tool retrieves historical weather from the ERA5 reanalysis archive (1940–present), with a specific verb and resource. It distinguishes itself from siblings by explicitly naming openmeteo_get_forecast as the alternative for recent dates, and the variable-name compatibility with the forecast API further clarifies its role.

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

Usage Guidelines5/5

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

The description provides explicit when-to-use guidance: it requires start_date and end_date, and explicitly states that for dates within the last week, openmeteo_get_forecast with past_days should be used instead. It also notes the requirement of at least one of hourly_variables or daily_variables, and explains the spill-to-canvas behavior for large ranges, giving clear context for when this tool is appropriate.

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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