clima_previsao

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

No annotations are provided, so the description carries the burden. It discloses the data source (Open-Meteo), that no token is needed, and that it performs geocoding. This gives the agent a good sense of behavior. It does not mention read-only or rate limits, but the read-only nature is implied by 'previsão' and the open API.

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?

Three short sentences, each providing essential information: function, source, and a key behavior (geocoding). There is no redundancy or unnecessary text.

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?

With an output schema present (not shown but indicated), the description does not need to explain return values. It covers the input parameters well, notes the default for 'dias', and explains the geocoding step. It is complete enough for this relatively simple tool.

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 coverage is 100%, so the schema already describes both parameters. The description adds value by explaining that the city name is resolved via geocoding and provides examples. This contextual information helps the agent understand how parameters are used beyond the schema.

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 the tool returns weather forecasts for Brazilian cities. It is distinct from sibling tools which are all agricultural (cotacao, noticias, etc.), so there is no ambiguity about purpose.

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 context (city name, data source) but does not explicitly state when to use over siblings. However, the purpose is so distinct that usage is implicitly clear. No when-not-to-use guidance.

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