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

The description adds meaningful behavioral context beyond the annotations: it discloses that queries require prepaid credits, that no platform credentials are needed (though external login params exist), that data is non-confidential, and that the client is responsible for LGPD compliance. These are important operational and legal details not captured in annotations.

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 brief (around 4 sentences) and each sentence contributes value—purpose, payment, data classification, legal responsibility. However, it begins by repeating the tool name, which is redundant, and the structure is a single dense block rather than clearly separated concerns. Overall, it is efficient and not padded.

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 the tool's complexity (27 parameters, no output schema, no parameter descriptions), the description is drastically incomplete. It does not explain what the tool does operationally, what the required inputs signify, how to structure them, or what response format to expect. An agent would be unable to invoke this tool correctly without external documentation.

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 description offers no explanation of any of the 27 parameters. The schema has 0% coverage, and the tool is heavily parameterized with 19 required fields. Without any description of what fields like 'chassi', 'categoria', 'numero_espelho', or 'nome_anterior' mean or their format, the description fails to assist an agent in constructing a valid request.

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 states the tool performs a 'consulta' (consultation) of 'Documentos Transferência de Município' but does not clearly specify what exact data or status is returned. It mentions it queries official Brazilian sources, but the scope remains ambiguous—e.g., whether it returns vehicle transfer status, registration details, or something else. While it distinguishes from generic sibling tools by its domain, it lacks a sharp verb-object definition.

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 no guidance on when to use this tool versus alternatives. It does not mention any exclusion criteria or context where this tool is preferable. It only notes that payment is required and that it accesses official data, which is contextual but not usage direction.

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