reapresentacoes

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

With no annotations supplied, the description bears the full burden of disclosing behavior, and it does well: it explicitly explains that the originally published numbers that normally disappear from databases are retained here, and it says what will be returned for each affected account. It could add operational details such as pagination, limits, or whether no results are returned for tickers without restatement, but the writing already communicates the tool's distinctive behavior clearly.

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 and front-loaded: the introductory sentence conveys the essence immediately, followed by behavioral notes, use cases, and parameter definitions. It is somewhat longer than necessary, but every sentence adds meaningful information for a potential data-retrieval tool, and the parameter section is highly useful. The structure is logical, with no filler or repetition that would harm an agent's parsing.

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?

Given that there is no output schema, the description does enough by summarizing the return content (original and new values with publication dates, per affected account) and by explaining scenarios suited to the tool. Missing pieces include formal response format, behavior on tickers with no restatement, and explicit guidance on when this tool is not the one to use. As a whole, an agent can invoke the tool correctly with confidence, and an essential data is present.

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?

The schema provides 0% description coverage, so the entire semantic weight is on the description. It fully compensates: `ticker` is clarified as a B3 code, uppercase, with class digit and examples; `chave_api` explains when it is optional, when required, which ticker works as a free sample, and how the environment variable fallback behaves. This is exactly the kind of parameter guidance agents need when the schema is unhelpful.

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's function: it returns restated financial statements with both the original and reissued versions ('Devolve, por conta afetada, o valor da versão original e o da versão nova, com as datas das duas publicações'). The resource is identifiable as 'Balanços que a empresa republicou depois', and the emphasis on old numbers staying visible distinguishes it from standard financial-report tools, even though it does not explicitly name a sibling. A 4 is appropriate because the objective is clear, but sibling differentiation is implicit rather than explicit.

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 contexts: it 'Serve para auditar mudança de histórico e para saber se um backtest rodou sobre números que depois foram revistos'. This gives an agent an actionable sense of when to invoke the tool. However, it does not state when not to use it or mention alternatives such as `fatos_contabeis` or `indicadores_anuais` for current data, so it falls short of the explicit when/not/alternatives standard.

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