DETRAN AL: Infrações, 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 readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context: payment model (prepaid credit), lack of platform credentials, the official/non-confidential nature of the data, and LGPD responsibility for the client. This goes beyond the annotations by explaining operational and legal aspects.
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 a compact 4-sentence paragraph, front-loaded with the main purpose and followed by relevant operational and legal context. Each sentence earns its place, covering the query, hosting, payment, data source, and LGPD without unnecessary verbosity.
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 description provides good context about the source, payment, and legal aspects, but it does not describe what the query returns (e.g., a list of violations with details). With no output schema present, the description should clarify return values, but it remains implicit. This is a notable gap for a complete understanding of the tool.
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%, so the description must compensate for parameter semantics. The description does not mention 'placa' or 'renavam' at all, providing no additional meaning beyond the schema. The parameter names are somewhat self-explanatory in the Brazilian context, but the description adds zero value for parameter understanding.
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: 'DETRAN AL: Infrações, consulta em fonte oficial' – querying traffic violations from the official DETRAN AL source. The verb 'consultar' and resource 'infrações' are specific, and the tool is clearly distinguished from sibling tools (authenticate, connect, etc.) which are generic platform operations.
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 clear usage context: it is hosted by the platform, requires no platform credentials, and is paid per query with prepaid credit. It also clarifies that the data source is the same as available to citizens, which implies when it should be used. No explicit alternatives are mentioned, but no domain-specific alternatives exist among the sibling tools.
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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