SEFAZ PI: IPVA, 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 | ||
| cnpj | No | ||
| chassi | No | ||
| renavam | No | ||
| tipo_cotas | 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds meaningful context by stating that platform credentials are not needed, each query consumes prepaid credit, and the returned data is non-confidential official information subject to LGPD compliance. This enriches the agent's understanding without contradicting any annotation.
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 concise and logically structured: the core purpose is stated first, followed by details about hosting, payment, data nature, and legal responsibility. Although it omits parameter descriptions, that is more a completeness gap than a conciseness issue; the text itself is tightly written.
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?
With no output schema and no explanation of inputs, the description leaves the agent without essential invocation details. It covers legal and payment context, but lacks parameter semantics and response expectations, so the tool is not fully usable based on this description alone.
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?
The input schema defines five properties (cpf, cnpj, chassi, renavam, tipo_cotas) with zero schema descriptions, and the description provides no explanation of their meanings or usage. The agent cannot determine what 'tipo_cotas' represents or which identifier to supply, making correct parameter selection nearly impossible.
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 'SEFAZ PI: IPVA, consulta em fonte oficial' – a specific query operation for IPVA data from the Piauí state tax authority. This clearly distinguishes it from sibling platform tools like authenticate, connect, and marketplace, which serve different purposes.
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 mentions that the tool is hosted by the platform, requires no platform credentials, and is paid per query with prepaid credit, implying use when official IPVA data is needed and credits are available. However, it does not explicitly state when not to use the tool or name alternative query methods, so the guidance remains implied rather than explicit.
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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