DETRAN PA: Veículo, 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. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint, idempotentHint), the description discloses that the tool is pay-per-query with prepaid credit, requires no platform credentials, and accesses non-confidential, citizen-available official data. It also notes LGPD responsibilities, which are not captured in any annotation. These behaviors influence invocation decisions and cost awareness.
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 front-loads the main purpose but then becomes a single, comma-heavy, run-on sentence mixing operational, legal, and data-source details. Information like 'fonte oficial' is repeated, and the structure could be clearer with separate sentences or bullet points. It is not overly long but lacks conciseness due to redundancy.
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 description covers purpose, cost, data source, and legal obligations, but it does not describe the return value or any error/edge cases. Since there is no output schema, the description should indicate what the query returns (e.g., vehicle data structure) to avoid ambiguity. The geographic scope is implied by 'DETRAN PA', which is in the tool name and opening.
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 lists 'placa' and 'renavam' with no descriptions, and the description does not explain their meaning, format, or required patterns. Although the description mentions 'veículo', it provides no additional context for these parameters, leaving the agent to rely on external knowledge. Schema description coverage is 0%, and the description fails to compensate.
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 'DETRAN PA: Veículo, consulta em fonte oficial', which clearly states the tool queries vehicle information from DETRAN PA's official source. This distinguishes it from sibling tools like 'authenticate' and 'marketplace', which serve entirely different purposes.
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 implies usage (querying official vehicle data) but does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or conditions. Sibling tools are generic platform operations, so there are no direct competing vehicle-query tools, but the description offers no explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.