DETRAN RR: Débitos, 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 |
|---|---|---|---|
| ait | No | ||
| 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 readOnlyHint=true, idempotentHint=true, destructiveHint=false, which cover safety. The description adds that this is non-confidential data and that the client is the data controller per LGPD, providing additional context about data handling and legal responsibility, which goes beyond the 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?
Description is one concise paragraph, well-structured, front-loaded with the tool's purpose and context. No wasted words.
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 there's no output schema and annotations are minimal, the description covers the essential behavioral aspects (payment, official source, LGPD). It could be more complete about what data is returned, but for a query tool with read-only hints, it's sufficient.
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 describes parameters: placa, renavam, and ait (optional). Description doesn't add extra parameter details. Schema coverage is 0%, so description could have compensated, but it doesn't. With 3 parameters, the schema itself is clear (placa and renavam are required, ait is optional). Baseline 3 is fair.
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 purpose: querying DETRAN RR debts from an official source. It specifies the resource (vehicle debts for Roraima) and the verb (consult). This distinguishes it from generic tools like 'authenticate' or 'connect', making it clear this is a data lookup tool.
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 legitimate purposes (with LGPD compliance mention) and mentions payment with prepaid credit, which sets expectations. However, it doesn't explicitly state when to use this tool versus alternatives; there are no obvious sibling tools for debt queries, so it's adequate for context.
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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