CENPROT SP: Protestos, 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 |
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 important behavioral details beyond annotations: it mentions the payment model (prepaid credit per query), privacy classification (non-confidential data), and legal responsibility (LGPD). These are not covered by the readOnly/idempotent hints, so the description adds value. However, it omits potential rate limits or error behaviors.
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 somewhat redundant, repeating that it is a consultation and legal statements about LGPD in two sentences. It could be tightened to one or two sentences without losing meaning. Still, it is not excessively long.
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?
No output schema is provided, and the description does not describe what the response contains (e.g., list of protests, status codes, pagination). It also lacks information about error conditions or how to interpret results. This leaves significant gaps for the agent to understand the tool's full behavior.
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 provides no descriptions for 'cpf' and 'cnpj', and the description does not explain their roles or accept/reject criteria. It only implies they are identifiers for the consultation. Given 0% schema coverage, the description should have clarified these parameters but did not, leaving the agent guessing.
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 indicates this is a consultation tool for protests (protestos) from an official source, which distinguishes it from unrelated sibling tools like authenticate or report_bug. The noun-verb structure is effective, though it doesn't explicitly state 'query by CPF/CNPJ'.
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 some context (official source, prepaid credit, no platform credentials) but does not explicitly state when to use this tool over alternatives or outline specific conditions (e.g., required parameters, eligibility). Since there are no similar tools, this is acceptable but not exemplary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.