Conselho Regional de Odontologia TO: 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 readOnlyHint=true and destructiveHint=false, so the description doesn't need to restate that. It adds context about payment per query, prepaid credits, and LGPD data control responsibilities, which is useful. It doesn't contradict annotations; it complements them with operational details.
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, but dense with relevant operational info: official source, payment model, data nature, LGPD disclaimer. It's well-structured and front-loaded with the main purpose. Every sentence adds value; no fluff.
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 with one parameter and no output schema. The description covers purpose, data source, and legal context. It doesn't mention return format, but that's not required given the simplicity. The LGPD note adds compliance context that is important for usage.
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 description doesn't explain the parameter 'inscricao' beyond what the schema shows (a string). Schema coverage is 0%, so the description could have elaborated, but it doesn't. However, the purpose is clear and the parameter name is self-explanatory (registration number). This is a minor gap.
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 consults dental council records (CRO-TO) from an official source, with a specific verb ('consultar') and resource (Cadastro). It distinguishes from siblings by emphasizing it is a data consultation from official Brazilian sources, not related to authentication or marketplace operations.
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?
It explains when to use: to consult official public data, with an explicit disclaimer that it is not confidential, and notes it is hosted by the platform with pay-per-query using prepaid credits. It doesn't explicitly contrast with siblings, but the context of official data consultation is clear.
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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