meteo_forecast

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

Annotations already declare readOnlyHint=true and openWorldHint=true, but the description adds substantial behavioral context: the hybrid model source (ICON for first 5 days, best_match beyond), geocoding with fuzzy fallback, hourly data limitation to 48 hours, and direct-link fallback on upstream failure. It also discloses that responses include provenance fields (`modell`/`modell_details`). No contradiction with annotations.

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 well-organized with clear sections (use_case, important_notes, example, args, returns). Every section contributes value—purpose, behavioral nuances, invocation examples, and parameter explanations—without redundancy or padding. It is appropriately sized for the tool's complexity.

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?

The description covers all critical aspects: model provenance, geocoding fallback, hourly data range, failure handling, and response format. With an output schema present, the concise Returns statement suffices. The tool's complexity is fully addressed, leaving no significant gaps for an agent to misuse it.

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 top-level schema parameter has no description (0% coverage), the description explicitly enumerates each field under 'Args' and adds meaning beyond the nested schema: e.g., 'lat/lon überschreibt location und spart einen HTTP-Roundtrip' and explanation of response_format. The important_notes also clarify the geocoding behavior and hourly scope, fully compensating for the schema gap.

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 opens with a clear use case: '1-16 Tage Wetterprognose für einen Ortsnamen oder Koordinaten' and specifies output (daily values, optional hourly). It names the unique MeteoSwiss ICON model and lists distinct weather fields, making the tool's purpose unmistakable and distinguishing it from siblings like meteo_current or meteo_warnings.

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 description provides clear context for when to use (multi-day forecast, up to 16 days) and includes practical usage tips in important_notes (e.g., lat/lon overrides location and saves a roundtrip; hourly only covers first 48 hours). However, it does not explicitly mention alternatives or when-not-to-use scenarios, leaving sibling differentiation implicit.

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