DETRAN MG: TRLAV, 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 |
|---|---|---|---|
| ano | Yes | ||
| renavam | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context: pay-per-query billing, no platform credentials, official-source data, and LGPD data-controller responsibility. It does not describe response format or failure modes, but those are not required by the 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 only three sentences and front-loads the core purpose before addressing payment and legal terms. It is reasonably tight, though the LGPD sentence is somewhat boilerplate and the mixed clauses could be structured more cleanly.
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 2-parameter read-only tool with good annotations, the description covers source, billing, and compliance. It does not state what fields or data the consultation returns, nor does it decode the TRLAV acronym, so an agent cannot fully predict the output.
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 gives no explanation of 'renavam' or 'ano'. A domain-savvy agent may infer RENAVAM is a Brazilian vehicle identifier and that 'ano' is a year, but the tool does nothing to clarify formats, constraints, or which year (model, manufacturing, licensing).
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 opens with 'DETRAN MG: TRLAV, consulta em fonte oficial' and identifies the tool as a consultation against an official Brazilian source, which gives a clear verb+resource pairing. It does not expand the TRLAV acronym or state exactly what record types are returned, so it is clear but slightly underspecified.
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 useful context: the tool is hosted by the platform, requires no platform credentials, uses prepaid credits, and queries official public data. However, it never explicitly says when to choose this tool over siblings like connect or marketplace, or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.