DETRAN MA: Multas, 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?
The annotations already cover readOnlyHint, idempotentHint, and destructiveHint, so the description adds complementary context: the tool is hosted by the platform, no platform credentials, payment per query, and relevant data privacy legal statements (LGPD). It does not add any misleading or contradictory behavior details.
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 starts with the concise purpose, then adds necessary operational, financial, and legal details. It is not bloated, but repeated references to 'official sources' and LGPD have slight redundancy. Overall, each sentence conveys important context for a paid Brazilian data-consultation tool.
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?
Given the tool is very simple (two parameters, no output schema), the description covers the main intent, payment model, and legal conditions. However, it lacks details about output format, any notification of missing results, or possible failure states such as invalid placa or renavam. The completeness is a moderate baseline with clear 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 coverage is 0%: descriptions are absent for placa and renavam. The description does not mention or explain these parameters, discuss formats, or provide examples. It expects the agent to infer that placa is the license plate and renavam is the Brazilian vehicle registration number; this ad hoc knowledge is implicit and not supported by the tool description.
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 explicitly states the action: consultar multas (query fines) from a specific resource: DETRAN MA, an official source. It uses a specific verb and resource, and clearly differentiates from the sibling tools like authenticate, marketplace, and report_bug by describing a niche municipal/government data-lookup service.
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 gives useful context for when to use this tool: for consulting official Brazilian traffic fines, paying via prepaid credit, and with no platform credentials required. It does not definitively contrast with alternatives, because none of the sibling tools offer a comparable consultancy, so it is more clear context than optional exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.