SINTEGRA: SC, 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 |
|---|---|---|---|
| ie | No | ||
| cpf | No | ||
| cnpj | 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 indicate readOnlyHint: true, idempotentHint: true, and destructiveHint: false, and the description aligns by mentioning it's a query that returns data available to citizens without confidential info. It adds useful context about data controller responsibilities (LGPD), though it doesn't specify return format, rate limits, or exact data fields returned.
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 composed of two concise sentences that pack meaningful context: official source, no credentials, prepaid credits, citizen-level data, and LGPD compliance. While dense, it covers key points without unnecessary fluff.
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 complexity of regulatory and payment context, the description covers legal and operational aspects well, but lacks what the actual response contains, example usage, or preconditions (e.g., sufficient credit). For a tool with no output schema, more detail about return data would be helpful.
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 description mentions the tool queries official sources and lists 'cpf', 'cnpj', and 'ie' (state ID) parameters in the schema, but the description doesn't explain which parameter to use when, or that exactly one is required. It doesn't compensate for the 0% schema coverage, leaving the agent guessing about semantics like state registration number.
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 identifies the tool as consulting official Brazilian sources (SINTEGRA SC) and mentions it involves queries ('consulta'), but the purpose remains diffuse. It doesn't clearly state what a user achieves by calling it (e.g., get a taxpayer's registration status).
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 provides clear context about when to use the tool, including that it queries official sources without platform credentials, prepaid credits per query, and that it aligns with LGPD for legitimate purpose. This distinguishes it from sibling tools by emphasizing the official source and payment model.
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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