SRF Meteo – Standort suchen

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds valuable context beyond annotations: it explains that the geolocationId improves forecast quality compared to coordinates, and reiterates the Swiss scope. It does not contradict annotations and provides useful operational nuance.

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 structured with use_case, important_notes, and example sections. Every part serves a purpose: the core sentence states functionality, use_case gives context, important_notes adds scope and benefit, and examples clarify parameter values. It's slightly longer than necessary but remains efficient and front-loads the core purpose.

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 single-parameter search tool with an output schema (not shown), the description is complete: it explains what it returns (list with geolocationId), when to use it, the Swiss scope, and gives concrete examples. An agent has everything needed to invoke it correctly and interpret its role in the forecast workflow.

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?

Although the signal reports 0% schema description coverage, the schema actually includes a description for the query property. The tool description reinforces the format with examples (query='Zürich', '8001', 'Lausanne') and explicitly mentions 'Name oder Postleitzahl'. This compensates well for any perceived coverage gap and clarifies acceptable input patterns beyond the regex.

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 action (search Swiss locations), the resource (weather forecast locations), and the output (list with geolocationId). It distinguishes itself from sibling weather tools by explicitly positioning itself as the first step to obtain a precise geolocationId before calling forecast tools. No ambiguity.

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?

The use_case tag explicitly says this is the first step before srgssr_weather_current, srgssr_weather_forecast_24h, or srgssr_weather_forecast_7day to get the precise geolocationId. This gives clear when-to-use guidance and names the alternatives. The important_notes also sets scope (Swiss locations only), preventing misuse for non-Swiss queries.

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