Prefeitura PE Petrolina: Multas, 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 |
|---|---|---|---|
| placa | Yes | ||
| renavam | 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, and the description adds meaningful behavioral context: the query is hosted by the platform, uses prepaid credits, does not require platform credentials, accesses non-confidential public data, and imposes LGPD data-controller responsibilities on the client. This goes beyond annotation basics, though it does not describe return format or error 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 concise and front-loaded, starting with the core purpose and then adding payment/legal context. Each sentence provides relevant information, though some legal phrasing could be considered boilerplate. It remains appropriately sized for the tool's complexity.
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 simple two-string input, strong annotations, and unrelated sibling set, the description covers the essential operational details: official source, prepaid payment, no credentials needed, non-confidential data, and LGPD responsibilities. It lacks parameter-level guidance and output shape, but for a straightforward read-only query tool, it is reasonably complete.
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 description coverage is 0%, and the description does not compensate by explaining the required parameters 'placa' and 'renavam' or their expected formats. While the parameter names are self-descriptive, the description adds no semantic value beyond the schema itself, which is a significant gap for a low-coverage case.
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 specifies the tool's purpose: consulting traffic fines ('Multas') from the official Prefeitura de Petrolina source. It uses a specific verb ('consulta') and resource ('Prefeitura PE Petrolina: Multas'), distinguishing it from unrelated siblings like authenticate, marketplace, and 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 usage context: it is for official fines consultation, payable per query with prepaid credits, and requires no platform credentials. However, it does not explicitly state when to use this tool over alternatives or provide any exclusion criteria, leaving usage guidance largely implicit.
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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