Secretaria de Inspeção do Trabalho: CAEPI, 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 |
|---|---|---|---|
| ca | 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?
Annotations already cover read-only and idempotent. The description adds valuable context: payment model (prepaid consultation), no platform credentials needed, data is non-confidential, and client is LGPD data controller. Contradicts no annotation. It doesn't cover error handling or rate limits, but what's added is meaningful.
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?
Description is ~4 sentences, covering purpose, hosting, payment, and legal responsibility. It's somewhat verbose but relevant. The key purpose is stated first, but the legal and payment details could be shortened. Not overly long, but not tight.
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?
Despite low complexity (1 param, no output schema), the description fails to explain the required 'ca' parameter, the format of data returned, or any usage constraints like rate limits. With low schema coverage (0%), the description needed to fill that gap and didn't.
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 1 required parameter 'ca' with zero description coveragecars. The tool description does not explain what 'ca' represents, its format, or expected values. Without that, the agent cannot correctly provide input.
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 a consultation of CAEPI via official Brazilian sources, using the verb 'consultar' (consult) and a specific resource name. It distinguishes from siblings which are generic toolkit utilities, though it could be more explicit about what data is returned.
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?
No explicit guidance on when to use or not use this tool. The description provides context (official source, pay-per-use, not secret data) but does not mention alternatives or exclusions. Sibling tools are unrelated, so it's implied but not stated.
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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