DETRAN AP: 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?
The annotations already mark the tool as read-only and idempotent; the description adds meaningful behavioral context beyond that: prepaid credit cost per query, no platform credentials to the official source, the data is not confidential, and the client is the data controller under LGPD. No annotation contradiction is present.
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 compact and front-loaded with the main purpose, followed by operational and legal details. The LGPD sentence is a bit dense but provides important usage constraints; overall there is minimal waste.
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?
For a simple read-only two-parameter query tool, the description covers purpose, official source, pricing model, credentials, data sensitivity, and legal responsibility. It would be more complete with parameter format details or expected return shape, but the description is reasonably 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 schema has 0% description coverage, and the tool description does not compensate by explaining the expected format or meaning of 'placa' and 'renavam'. The description only gives general vehicle-query context, leaving parameter invocation underspecified.
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 explicitly states the action ('consulta') and the resource ('veículo DETRAN AP') and adds that it queries an official source. This clearly identifies the tool's purpose and distinguishes it from the unrelated sibling tools such as authenticate, marketplace, and toolkit_info.
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 provides practical usage context: it is an official vehicle query, paid by prepaid credit, requiring no platform credentials for the external source, and subject to LGPD responsibility. However, it does not explicitly state when to use the tool versus alternatives or what conditions should prevent its use.
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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