DETRAN AM: Licenciamento Anual, 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?
Annotations already declare it read-only, idempotent, and non-destructive. The description adds useful non-annotation details: payment requirement, data from official public sources (not confidential), and LGPD responsibility. No contradiction with 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?
The description is moderately wordy, covering multiple operational and legal aspects in three sentences. It front-loads the purpose but includes extra details (LGPD, payment) that, while useful, could be more succinct for an agent.
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 has only two parameters and lacks an output schema, so the description should explain what the response contains (e.g., vehicle licensing status). It fails to do so, and also omits parameter hints, leaving the agent under-informed for a successful invocation.
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?
Schema coverage is 0%, and the description gives no explanation of 'placa' or 'renavam'. It doesn't compensate for the lack of parameter documentation, leaving the agent to infer meaning from cryptic names.
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 'Licenciamento Anual' (annual licensing) from DETRAN AM, using the verb 'consulta' and specifying the resource. It distinguishes itself from unrelated sibling tools by focusing on a specific Brazilian state agency's data.
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?
It provides operational context: hosted on the platform, no platform credentials needed, pay-per-query with prepaid credit. It doesn't explicitly discuss alternatives, but no similar siblings exist, so the guidance is adequate for an isolated tool.
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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