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open-meteo-mcp-server

by io.github.cyanheads

Openmeteo Get Flood

openmeteo_get_flood
Read-onlyIdempotent

GloFAS (Global Flood Awareness System) river discharge forecast and historical reanalysis. Returns daily ensemble river discharge (m³/s) for the river nearest to the given coordinates — no river ID needed, the API snaps to the nearest stream. 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 DataCanvas for SQL querying when canvas is enabled, returning a bounded preview with truncated: true when it is not.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
end_dateNoEnd date for historical reanalysis (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.
latitudeYesLatitude in decimal degrees. The API snaps to the nearest river — no river ID required. Use openmeteo_search_locations to resolve a place name.
timezoneNoIANA timezone or "auto". Default "auto".auto
canvas_idNoDataCanvas token for wide reanalysis queries. When a result is too large to return inline — driven by total payload size, so a 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_dateNoStart date for historical reanalysis (YYYY-MM-DD, e.g., "2023-01-01"). GloFAS reanalysis covers from 1984-01-01. Requires end_date — the pair must be sent together, and neither combines with forecast_days.
forecast_daysNoNumber of forecast days ahead (1–210). Mutually exclusive with start_date/end_date — omit it entirely when pulling a historical range.
daily_variablesNoDaily discharge variables to fetch (e.g., ["river_discharge", "river_discharge_p25", "river_discharge_p75", "river_discharge_min", "river_discharge_max"]). Required.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dailyNoPer-day records with "time" (YYYY-MM-DD) + one key per requested variable containing discharge in m³/s, or null for coordinates outside GloFAS coverage. 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.
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 (nearest river 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.
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.
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 (e.g., {"river_discharge": "m³/s"}).
record_countNoTotal number of daily discharge records — the full staged count when truncated is true, not the length of the daily preview.

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the read-only and idempotent hints in annotations, the description discloses several important behaviors: it returns null for coordinates far from any river or without GloFAS coverage, it spills to DataCanvas for large reanalysis queries with a bounded preview and truncated flag, and it enforces mutual exclusivity of modes. It also explains the snapping behavior to the nearest stream. These details go well beyond annotations and give an agent predictable expectations about output and side-effects, with no contradiction.

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 comprehensive yet tightly packed. It leads with the core purpose, then the mode selection, then the variable list, and finally edge-case behaviors. Every sentence contributes new information; there is no fluff or repetition. The structure is logical and front-loaded, allowing a quick scan for the most critical information. Despite its length, it is efficient and earns its place.

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?

For a complex tool with 8 parameters, a output schema, and multiple interactive behaviors, the description covers all necessary aspects: input coordinates, mode usage, supported variables, output limitations (null return), scaling behavior (spill to DataCanvas), and the existence of output schema means return values need not be restated. The agent has enough to call it correctly and anticipate outcomes in both normal and edge cases.

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?

The schema already covers all 8 parameters at 100% coverage, so a baseline of 3 applies. The description adds meaningful semantic context beyond the schema: it explains why canvas_id is relevant (spill for large results), details the mutual exclusivity of forecast_days vs. start_date/end_date, lists example daily_variables, and clarifies that a date range requires both endpoints. This extra explanation helps the agent reason about parameter combinations and values, justifying a score above baseline.

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 opens with a precise resource and action: 'GloFAS (Global Flood Awareness System) river discharge forecast and historical reanalysis.' It further clarifies it returns daily ensemble river discharge for the nearest river to given coordinates, immediately distinguishing it from weather, climate, marine, or other Open-Meteo tools. The wording is specific and not a tautology, and the mention of 'no river ID needed' adds a differentiating nuance.

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?

The description clearly states the tool's purpose and the two mutually exclusive modes (forecast vs. historical reanalysis), which is strong context for usage. It does not explicitly name alternatives like openmeteo_get_forecast or openmeteo_get_historical, nor does it say 'do not use for X.' However, the specificity of 'river discharge' and 'GloFAS' makes the intended use clear, so the lack of explicit exclusion clauses is a minor gap. This aligns with the 'clear context, no exclusions' anchor.

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