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open-meteo-mcp-server

by io.github.cyanheads

Openmeteo Dataframe Describe

openmeteo_dataframe_describe
Read-onlyIdempotent

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
canvas_idYesCanvas ID returned by openmeteo_get_forecast, openmeteo_get_historical, openmeteo_get_marine, openmeteo_get_air_quality, openmeteo_get_ensemble, openmeteo_get_flood, or openmeteo_get_climate when truncated: true.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
tablesNoTables and views registered on this canvas.
canvas_idNoCanvas ID that was inspected.
expires_atNoISO 8601 expiry after the sliding 24 h TTL.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, and the description adds meaningful operational context: the tool only applies to DataCanvas objects staged by specific openmeteo_get_* calls. The 'List' wording is consistent with the read-only and idempotent behavior declared by 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.

Conciseness5/5

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

The description contains two focused sentences: the first states what the tool does, and the second states the recommended call order. It is concise, front-loaded, and contains no filler or redundancy.

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 one-parameter tool with an output schema and read-only/idempotent annotations, the description provides the necessary workflow context: how the DataCanvas is staged, what the tool returns, and how it relates to openmeteo_dataframe_query. Nothing essential is missing.

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%, and the input schema already explains that canvas_id must come from a truncated-result run of the openmeteo_get_* tools. The tool description adds no new parameter-level semantics, but none are needed because the schema fully covers the single parameter.

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 uses a concrete verb-resource pair ('List the tables and their columns on a DataCanvas') and names exactly which staging tools produce the relevant data. It also differentiates the tool from openmeteo_dataframe_query, so an agent can identify it without opening the schema.

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 an explicit workflow instruction: 'Call this first to discover table names before querying with openmeteo_dataframe_query.' This clearly tells the agent when to use this tool and which sibling tool to use afterward.

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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