MDA CAF: Unidade Familiar de Produção Agrária (UFPA), 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 | 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 establish readOnly, idempotent, and non-destructive behavior. The description adds critical behavioral context: payment per query with prepaid credit, no platform credentials, data is public/non-confidential, and the client is data controller under LGPD. This goes well beyond what annotations provide.
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 concise with four sentences, front-loading the main purpose. It packs relevant operational and legal details without unnecessary repetition, though the structure could be slightly improved by separating payment/legal specifics into a clearer sequence.
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 is simple (one parameter, read-only), and the description explains the data source, payment, and legal responsibility. However, it lacks any indication of the expected return data or examples of input, which is a notable gap given no output schema exists.
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?
Schema coverage is 0% and the description does not mention the 'cpf' parameter at all. The agent receives no explanation of what CPF is, its format, or how to use it, leaving the input completely underspecified.
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 explicitly states it is a query ('consulta') for 'Unidade Familiar de Produção Agrária (UFPA)' from the official MDA CAF source. It clearly distinguishes itself from sibling platform utilities (authenticate, connect, etc.) by naming the specific resource and operation.
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 usage context: it is hosted by the platform, requires prepaid credit, and does not need platform credentials. It also clarifies the legal context (LGPD). While it doesn't explicitly state when not to use it, the sibling tools are unrelated, so the guidance is sufficient.
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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