DETRAN PB: Guia de Pagamento, consulta em fonte oficial. Hospedado pela plataforma, sem credenciais da plataforma, pague por consulta com crédito pré-pago. Consulta informação de fontes e órgãos oficiais brasileiros (a mesma disponível ao cidadão), não é dado sigiloso. O cliente é o controlador dos dados e responde pela finalidade legítima (LGPD).
| Name | Required | Description | Default |
|---|---|---|---|
| placa | Yes | ||
| renavam | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare it read-only, idempotent, and non-destructive. The description adds meaningful behavioral context beyond annotations: it requires prepaid credit, is hosted by the platform, does not need platform credentials, returns non-confidential official data, and places LGPD accountability on the client.
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 short and front-loaded with the core purpose ('Guia de Pagamento, consulta em fonte oficial'), followed by operational and legal context. Still, the LGPD/data-controller clause is slightly verbose, though not irrelevant for a data query tool.
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?
The tool has only two string parameters and read-only annotations, but lacks an output schema. The description comprehensive enough on the source/access model, but it does not describe what a 'guia de pagamento' result contains, nor does it compensate for the entirely undocumented parameter formats.
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 input schema has no descriptions and 0% schema description coverage. The description does not explain 'renavam' or 'placa', their expected formats, or how they relate to the payment slip query. The parameter names are only weakly self-explanatory for non-Brazilian audiences.
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 identifies the resource: 'Guia de Pagamento' from 'DETRAN PB', and the verb: 'consulta'. It also states the scope ('fonte oficial', 'órgãos oficiais brasileiros'), which distinguishes it from the unrelated sibling system tools.
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 conveys contextual usage: official-source query, paid per consultation, no platform credentials needed. However, it gives no explicit 'when to use this vs alternatives' or 'when not to use', so usage guidance remains mostly implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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