MTE: Relatório de Transparência Salarial, 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.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, which cover the safety profile. The description adds transparency about the business model (payment per query with prepaid credit) and data governance (customer is data controller under LGPD), which are valuable behavioral traits beyond the annotations. It also clarifies the data is not confidential. However, it doesn't mention rate limits or response format, so it doesn't go beyond what annotations provide in terms of operation behavior.
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 concise, but it packs several pieces of information (source, payment model, data nature, LGPD responsibility) without clear structure or front-loaded key details. It is not overly long, but the purpose could be stated more directly upfront. The information is relevant but could be better organized into sentences with clearer focus on the main action.
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 tool has only one parameter and no output schema, the description should explain what the tool returns and what the query entails. The description covers the data source and legal aspects, but it omits details on the return format, possible errors (e.g., if CNPJ is invalid), and any limitations. With a single required parameter, the description is decent, but it could be more complete by stating that it returns the salary transparency report data for the given CNPJ.
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 has one parameter 'cnpj' with no description, and schema description coverage is 0%. The description does not explain the format, length, or expected value of CNPJ (e.g., with/without punctuation, leading zeros). This is a critical gap because a wrong format could cause errors in querying the official source. The description should have provided at least a hint about the CNPJ format.
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 states it is a query tool for the MTE Salary Transparency Report ('Relatório de Transparência Salarial, consulta em fonte oficial'), which is a specific verb+resource. It also clarifies the data source (official Brazilian sources) and that it is not confidential, distinguishing it from other tools in the marketplace context. The sibling tools are general platform utilities, so this stands out as a specific data consultation tool.
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 indicates the tool is for querying official data and mentions payment per query with prepaid credit, which implies usage context (paid consultation). However, it does not provide explicit when-to-use vs. alternatives or when not to use it, and the sibling tools are not alternatives for this data lookup. It lacks explicit guidance on prerequisites or scenarios where this tool should be preferred or avoided.
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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