Prefeitura SP Campinas: CPOM, 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 |
|---|---|---|---|
| cnpj | 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 description adds behavioral details beyond annotations: payment per query, no platform credentials, data is not confidential, and the client is the data controller under LGPD. These align with the readOnlyHint and idempotentHint annotations and provide valuable context for invocation and compliance.
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 with the purpose, flowing logically through operational details. It covers essential aspects without redundancy, though the LGPD statement could be seen as extraneous but is relevant for legal context.
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 tool has a simple single-parameter input and lacks an output schema. The description explains the source, payment, and legal aspects but does not specify the structure or content of the response. For a straightforward query tool, this may be sufficient, but it leaves some ambiguity about expected results without an output schema.
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 schema defines a single 'cnpj' parameter with no description. The description does not elaborate on the parameter format or requirements, but the tool name and context make it clear that the query is based on CNPJ. Given the simplicity of a single well-known parameter, the lack of added semantic detail is acceptable.
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 clearly states the tool queries official data from Campinas city hall's CPOM system, specifying the source and that it is a consultation. It distinguishes from generic sibling tools by its specific purpose, though it does not detail what information CPOM contains or returns.
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 context that this is a paid, read-only query to official Brazilian sources and notes no platform credentials are needed. It does not explicitly state when to use this tool vs alternatives, but sibling tools are generic platform functions, so differentiation is implicit 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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