SENATRAN: Recall, 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 | No | ||
| chassi | No |
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 declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds extra context: no platform credentials, per-query billing, official non-confidential data source, and LGPD controller responsibilities. This goes well beyond the structured 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 compact, front-loaded, and every sentence carries distinct value: purpose, hosting/credential model, data scope, and legal responsibility. There is no wasted text or tautological padding.
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 two-parameter read-only tool with good annotations, the description covers the operational and legal context very well. The main minor gap is the lack of any stated return/output expectations, especially since there is no output schema to fill that gap.
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 never mentions 'placa' or 'chassi', nor does it give format, relevance, or usage guidance for the parameters. The agent must infer everything from the raw property names, so the description adds no parameter-level meaning.
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 identifies the tool as a SENATRAN recall consultation backed by an official source. The verb 'consultar' and resource 'Recall da SENATRAN' are explicit, and the purpose is distinct from 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 clear usage context: query official Brazilian recall data without platform credentials, paying per query with prepaid credit. It does not explicitly state when not to use it or name alternatives, but none of the sibling tools are recall-related, so the context is sufficient.
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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