get_historical_weather

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

No annotations are provided, so the description must cover behavioral traits. It mentions the Open-Meteo Archive API but does not disclose rate limits, data availability, mutation risks, or response details. The behavioral context is minimal.

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-structured with 'Args', 'Returns', and 'Example' sections. Every sentence adds value, and the example illustrates usage clearly. No wasted words.

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 parameters and return type well, but lacks explanation for hourly/daily variable formatting and error handling. Given no output schema, it provides a good but not exhaustive overview.

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?

With 0% schema description coverage, the description fully compensates by detailing each parameter's purpose, format (e.g., 'YYYY-MM-DD'), ranges (e.g., '-90 to 90'), and defaults. It also explains the return type as a Pydantic model.

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 'Get historical weather data from Open-Meteo Archive API,' distinguishing it from sibling tools like 'get_weather_forecast' (forecast) and 'batch_get_historical_weather' (batch). The verb 'Get' and resource 'historical weather data' are specific and unambiguous.

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 implicitly guides usage by specifying it retrieves historical data, contrasting with forecast-related siblings. However, it lacks explicit when-to-use and when-not-to-use instructions or alternative mentions.

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