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open-meteo-mcp-server

by io.github.cyanheads

Openmeteo Get Marine

openmeteo_get_marine
Read-onlyIdempotent

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 DataCanvas for SQL querying when canvas is enabled, and return a bounded preview with truncated: true when it is not.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
end_dateNoEnd date for the archive range (YYYY-MM-DD, inclusive). Must be on or after start_date. Requires start_date — the pair must be sent together, and neither combines with forecast_days or past_days.
latitudeYesLatitude of a coastal or ocean point. Use openmeteo_search_locations to resolve a place name. Inland points return near-zero wave values.
timezoneNoIANA timezone or "auto". Default "auto".auto
canvas_idNoDataCanvas token for wide past_days, archive-range, 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.
past_daysNoInclude this many days of past data before today (0–92). Use for recent history instead of a start_date/end_date range. Default 0. Must stay 0 when start_date/end_date are used.
start_dateNoStart date for the archive range (YYYY-MM-DD, e.g., "2024-07-01"). Real wave values go back to at least 2022. Requires end_date — the pair must be sent together, and neither combines with forecast_days or past_days.
forecast_daysNoForecast horizon in days (1–8). Omit for the upstream default of 7. Mutually exclusive with start_date/end_date — omit it entirely when pulling an archive range.
daily_variablesNoDaily marine summary variables (e.g., ["wave_height_max", "wave_direction_dominant", "wave_period_max"]). Daily names only — an hourly name such as wave_height belongs in hourly_variables and is rejected here; for a daily summary use its published aggregate (wave_height_max). At least one of hourly_variables or daily_variables required.
hourly_variablesNoHourly marine variables (e.g., ["wave_height", "wave_direction", "wave_period", "wind_wave_height", "swell_wave_height"]). Hourly names only — a daily aggregate such as wave_height_max or wave_direction_dominant belongs in daily_variables and is rejected here. At least one of hourly_variables or daily_variables required.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dailyNoPer-day summary records with "time" (YYYY-MM-DD) + variable keys (e.g., wave_height_max in meters, wave_direction_dominant in degrees, wave_period_max in seconds). 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) + one key per requested variable (e.g., wave_height in meters, wave_direction in degrees, wave_period in seconds). 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 endpoint reports a name it parsed but does not serve.
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.
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.
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., {"wave_height": "m", "wave_period": "s"}). 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

A4.8/5.0
Behavior5/5

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

Annotations declare readOnlyHint=true and idempotentHint=true, so the description's burden is to disclose behavior beyond that. It does so thoroughly: 'Returns per-timestamp records — each entry contains a "time" field plus one key per requested variable,' and it details the spill behavior ('spill to DataCanvas for SQL querying... return a bounded preview with truncated: true when it is not'). It also flags 'ocean_current_velocity is null for non-open-ocean coordinates.' These behavioral specifics add real value 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 lengthy (over 150 words), but each sentence conveys a distinct fact or constraint. It is well-structured with lists of variables and clearly marked warnings (mutual exclusivity, variable-name rejection, inland near-zero). No sentence is redundant; the information density is high. A small deduction for being longer than strictly necessary, though every part earns its place given the tool's complexity.

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 (10 parameters, multiple windows, variable types), the description is remarkably complete. It explains the forecasting horizon (up to 8 days), past_days (up to 92), archive ranges (back to 2022), the per-timestamp output format, the spill behavior, null conditions, and validation rules. With an output schema already present, there is no need to detail the return structure further. The description fully equips an agent to call the tool 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 coverage is 100%, so baseline is 3. The description adds meaningful semantics by explaining the mutual exclusivity between date-range and forecast/past_days, the requirement for both start_date and end_date together, and the distinction between hourly and daily variable names (e.g., 'wave_height' vs 'wave_height_max') with rejection warnings. It also enumerates common variables, which helps users pick correct values. While the schema already documents each parameter, the description enriches the semantics with usage nuances.

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 purpose: 'Marine wave and ocean conditions for a coastal or ocean coordinate' and enumerates the covered variables (wave height, period, direction, etc.). It clearly distinguishes itself from land-focused siblings like openmeteo_get_forecast or openmeteo_get_climate by emphasizing the marine domain. The verb and resource are explicit and unambiguous.

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 gives explicit when-to-use guidance: 'Best for open-ocean and coastal exposed points — sheltered inland waters return near-zero wave values.' It also clarifies when to choose between forecast_days and date-range windows, and describes the DataCanvas spill mechanism for large queries. It touches on when to use this tool versus avoiding it (inland points), and provides operational context for the archive range and recent history options.

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.

Resources