Open-Meteo — Pollen Forecast

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive, so the safety profile is known. The description adds valuable behavioral context: the specific output allergens, the 'current plus hourly forecast' nature, the model source ('Copernicus CAMS'), the licensing ('CC BY 4.0'), and that no authentication is required. This goes well beyond what annotations provide.

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

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

The description is three sentences, each purposeful: it establishes purpose and output, mentions applicable use cases, and then adds source/licensing context. It is front-loaded with the core function and contains no filler or repetition.

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

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

For a tool with four parameters and an existing output schema, the description covers everything an agent needs: what data is returned, the time range, the geographic scope, the source, and auth requirements. Annotations handle the safety profile, and the output schema handles return structure, so nothing essential is missing.

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

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

Schema description coverage is 100%, so each parameter (latitude, longitude, timezone, forecast_days) is already well documented. The description adds no new parameter-level detail beyond what the schema provides; it only reinforces that the tool covers global locations and forecast days, which is already implicit in the schema. Baseline 3 is appropriate.

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

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

The description clearly states a specific verb ('Get'), resource ('pollen concentrations'), and scope ('for any global location'). It enumerates exactly which allergens are returned (alder, birch, grass, mugwort, olive, ragweed) and the time span (current plus hourly forecast 1–7 days), fully distinguishing it from the air quality sibling tools.

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

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

It lists concrete use cases ('allergy apps, outdoor scheduling, health risk agents') that signal when to invoke this tool. It does not explicitly name an alternative tool or say 'use X instead', but it notes the data source (Copernicus CAMS) and that it is pollen-specific, making the selection context clear without needing exclusion wording.

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