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

With no annotations, the description carries the burden. It usefully states that the result is an estimate based on 'Dados IBGE POF' and 'estimativas 2026', which prevents treating values as exact or current. It does not disclose response shape, units, or handling of unknown cities.

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?

A single hyphenated sentence that front-loads the verb and object, then lists categories and data source. There is no filler and no repetition of schema details.

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 one-parameter lookup with no output schema, the listed categories effectively convey what result dimensions the agent can expect. The main omissions are the return format/units and error behavior for invalid or unknown cities, so it is not fully complete.

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 baseline is 3; the city parameter is fully documented with examples in the schema. The description only adds that the city is Brazilian, which matches the schema without providing meaningful new semantic detail.

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 the action 'Retorna' and the resource 'custo de vida estimado em uma cidade brasileira', then enumerates the components. This clearly distinguishes the tool from the many calculate_* siblings and from get_average_salary.

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?

No guidance is provided on when to prefer this tool over alternatives, and no exclusions or conditions are stated. The intended use case must be inferred entirely from the name and the generic return statement.

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