DETRAN PI: Multas, 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?
The description explicitly notes it is hosted by the platform, requires no platform credentials, and involves a pay-per-query model with prepaid credit, which goes beyond annotations. It also clarifies data is public (same as available to citizens), not confidential, and addresses LGPD controller responsibilities, adding valuable context beyond readOnlyHint and idempotentHint.
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 a single concise paragraph covering key operational and compliance points without fluff. It is front-loaded with the main purpose, but the compliance and payment details could be seen as slightly verbose for a tool description, though they earn their place for transparency.
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 simple schema (2 string parameters, no output schema), the description covers the essential aspects of purpose, data source, payment, and legal context. It lacks explicit mention of input format or expected output, but this is acceptable for a query tool with a simple schema and existing annotations.
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 has 0% description coverage, so the description must compensate, but it does not explain what 'placa' (license plate) or 'renavam' (vehicle registration number) specifically mean or their formats. The tool name implies 'multas' consult, but no parameter-level detail is given, leaving the agent to infer from common knowledge.
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 queries DETRAN PI for vehicle fines (multas) from an official source, which is specific and distinguishes it from generic siblings. While it doesn't name alternative tools for fine queries, the resource (DETRAN PI multas) is clearly identified.
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 for consulting official Brazilian vehicle fine information and highlights payment via prepaid credit, which is useful context. However, it doesn't explicitly state when to use this tool versus alternatives, such as other DETRAN state tools or general search tools, and doesn't mention prerequisites like valid plate and RENAVAM.
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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