ANP: Revendas GLP, 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 | 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 discloses that the tool is hosted by the platform, requires pre-paid credit per query, no platform credentials, and that data is public (non-confidential). It also notes the customer's data controller responsibility under LGPD. Annotations already state readOnlyHint=true and destructiveHint=false, so the description adds context about payment and legal responsibility without contradicting 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 a single paragraph that is moderately concise, including essential context about official source, payment, and LGPD. It is front-loaded with the main purpose and then details. However, it could be slightly more structured, but no redundancy is present.
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 simple tool (one parameter, no output schema), the description covers the key context: official data, payment model, legal compliance, and availability. It doesn't describe return structure, but without an output schema that isn't required. The main gap is the lack of parameter format guidance, but for a simple cnpj query, this is somewhat acceptable. However, considering the credit payment requirement, it would be helpful to mention cost or example values.
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 has only one parameter 'cnpj' with no description, and schema coverage is 0%. The description does not provide any specific detail about the cnpi format or validation, so it fails to compensate for the lack of schema documentation. Baseline 3 applies because there is only one parameter, but additional meanings are not provided.
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 identifies the tool as consulting ANP GLP resellers data from an official source, with a specific resource (ANP Revendas GLP) and a clear verb (consultar). However, it doesn't explicitly distinguish from potential sibling tools that might also access ANP data, though siblings provided are unrelated.
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 for when to use the tool (when needing ANP reseller data) but does not give explicit when-not-to-use guidance or mention alternatives. It implies usage for legitimate purposes with LGPD compliance, but lacks clear exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.