Procuradoria Geral do Estado BA: Dívida Ativa, 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 | ||
| renavam | 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?
Annotations already provide readOnly, idempotent, and non-destructive hints. The description adds valuable behavioral context beyond those: payment with prepaid credits, no platform credentials needed, data is not confidential, and the caller is LGPD data controller. There is no contradiction with 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 and front-loaded: first the subject/purpose, then cost and credentials, then legal/source caveats. Each sentence adds something, though legal repetition about LGPD could be trimmed.
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 covers important operational context (official source, cost, credentials, consent, LGPD) but lacks enough detail about input selection and output behavior. With no output schema and no parameter descriptions, the agent still has meaningful uncertainty about how to invoke it correctly.
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 explain the CPF and RENAVAM parameters, their formats, optionality, or relationship to a query. Since it does not compensate for the schema gap, an agent receives almost no semantic help for the inputs.
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 ('consulta') and the resource ('Dívida Ativa' from the Procuradoria Geral do Estado BA), adding that it queries an official source. This clearly distinguishes it from sibling tools like authenticate, marketplace, and show_version, and gives a concrete purpose even without reading the input schema.
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 useful context: no platform credentials required, prepaid per-query cost, official non-confidential source, and LGPD responsibility. However, it does not explicitly state when to prefer this tool over alternatives or when not to use it, so usage guidance is largely implicit.
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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