DETRAN RR: Gravame, 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 |
Changes observed during successful MCP inspections.
Input schema / properties / chassiRemoved value: -{
- "type": "string"
-}Input schema / properties / placaAdded value: +{
+ "type": "string"
+}Input schema / requiredPrevious value: -[
- "chassi"
-]New value: +[
+ "placa"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds useful context by stating that the data comes from official Brazilian sources, is not confidential, is the same data available to citizens, and that the client is the LGPD data controller.
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 efficiently organized: purpose first, then payment/access model, then data-source and legal context. Each sentence contributes relevant information, though the LGPD sentence could be considered slightly tangential for invocation behavior.
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 is simple and the annotations cover safety, but there is no output schema and the description does not explain the expected response format or the license-plate format. Payment and legal handling are covered, making this adequate but not fully complete.
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 only required parameter, 'placa', has no schema description and is not mentioned in the tool description. The domain implies it is a Brazilian vehicle license plate, but the expected format and any additional parameter semantics are left entirely to inference.
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 action and resource: 'DETRAN RR: Gravame, consulta em fonte oficial' identifies a query operation on DETRAN RR lien information. This differentiates it from the generic sibling tools like authenticate, marketplace, and report_bug.
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 gives concrete usage context: hosted by the platform, no platform credentials needed, and paid per query with prepaid credit. It does not explicitly list exclusions or alternative tools, but the context is clear enough for an agent to decide when to invoke it.
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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