DETRAN MT: Impressão de CRLV, 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 |
|---|---|---|---|
| cpf | No | ||
| cnpj | No | ||
| 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 readOnly/idempotent/non-destructive. The description adds valuable behavioral context beyond annotations: pay-per-query cost, non-confidential nature, and LGPD compliance responsibilities (client is data controller). This is meaningful supplementary information, though return format is not described.
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 concise (three sentences), front-loaded with purpose, and each sentence adds distinct information (purpose, cost/hosting, legal/confidentiality). No redundant wording, though it could be slightly more structured.
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?
With no output schema, the description should explain what the tool returns (e.g., a printable CRLV document), but it does not. It covers payment, confidentiality, and legal responsibilities, but lacks parameter relationship context (e.g., renavam required with placa) and any indication of response format. It is adequate for a simple query tool but leaves important gaps.
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 description coverage is 0%, and the description does not compensate by explaining parameter meanings, required combinations, or example values. The parameter names (placa, renavam, cpf, cnpj) are somewhat self-explanatory but lack Brazilian-domain context that would help an AI agent correctly use them.
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: 'Impressão de CRLV, consulta em fonte oficial' (CRLV printing, query in official source). This specifies a unique resource (CRLV) and action (consult/print), and it is easily distinguished from sibling tools like authenticate or 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 context that the tool requires prepaid credit and is hosted without platform credentials, and it clarifies the data is not confidential. However, it does not explicitly state when to use this tool over alternatives or provide exclusions, though siblings are unrelated platform utilities.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.