Taxa de Alfabetização dos Municípios — Censo 2022

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

Annotations already convey read-only, idempotent, non-destructive. The description adds behavioral specifics: the default query returns 27 capitals when no state is given, top_n defaults to 20 with max 100, and ordering can be inverted. It also exposes the underlying IBGE table/variable identifiers, which helps understand data provenance.

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 concise: a one-line purpose, a tight parameter list, and three example queries. No redundant or filler sentences.

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?

Despite lacking an output schema, the tool's output is straightforward (a ranked list of literacy rates), and the description covers all input dimensions, defaults, and edge cases. The examples provide sufficient guidance for typical use cases.

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 covers all three parameters, but the description enriches them with practical examples ('35'=SP, '31'=MG) and clarifies the meaning of 'ordenar' as 'melhor taxa' vs 'pior taxa'. The special behavior of omitting estado_id is also described, which is not captured in 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 opens with a clear verb-resource pair: 'Consulta a taxa de alfabetização (15 anos ou mais) por município com ranking.' It also specifies IBGE table 9543 and variable 2513, distinguishing it from other IBGE demographic tools like population or density.

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 explicit usage examples ('Mais alfabetizados em SP', 'Pior escolaridade na BA', 'Ranking das capitais') and explains when to omit estado_id (returns capitals). It doesn't name alternative sibling tools but gives enough contextual cues for an agent to choose this tool for literacy rate queries.

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