Conselho Regional de Odontologia PA: Cadastro, 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 |
|---|---|---|---|
| inscricao | 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 declare read-only, idempotent, and non-destructive hints. The description adds valuable context: the tool is hosted by the platform, requires payment with prepaid credits, returns non-confidential data, and places data controller responsibility on the client (LGPD). This goes beyond annotations without contradiction.
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 covers purpose, payment, data sensitivity, and legal compliance. While it is not excessively long, it includes some legal and payment details that could be condensed, and it lacks clear structuring. It is adequate 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?
With no output schema and only one parameter, the description should explain what the tool returns and how to use 'inscricao'. It does mention official source and non-confidential data, but omits details about response format, prerequisites, or examples. The incomplete parameter documentation and lack of output info make this insufficient.
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 only parameter 'inscricao' is not explained in the description at all, and schema description coverage is 0%. Since there is no other documentation, the agent cannot infer what value to provide. The description fails to compensate for the missing parameter meaning.
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 states the tool performs a consultation (consulta) on official registration data from the Pará Regional Council of Dentistry, using the specific verb 'consultar' and resource 'cadastro'. This is precise and distinguishes it from generic platform tools like 'authenticate' or 'report_bug'.
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 implies the tool is used for official registration queries, but does not explicitly state when to use it vs alternatives or when not to use it. It mentions payment via prepaid credits and LGPD compliance, which gives some context, but lacks clear usage boundaries.
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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