SEMA PA: Lista de Desmatamento Ilegal do Pará (LDI), 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 |
|---|---|---|---|
| car | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive. The description adds meaningful context: hosted by the platform, no platform credentials required, payment required, official source, not confidential data, and LGPD compliance. It enriches beyond annotations without contradicting them.
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 of about 4 sentences, efficient for the domain. It front-loads the core purpose and adds necessary caveats (payment, LGPD), but could be trimmed slightly; it's not overly verbose.
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 a single undocumented parameter, the description should explain what 'car' represents and what the response contains. It only provides high-level context (official source, legalities) but misses parameter semantics and expected output, making it incomplete for effective invocation.
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 never explains the 'car' parameter. It does not compensate for the lack of parameter documentation. The only clue is that it's a query about deforestation, but 'car' (likely Rural Environmental Registry) is not defined, leaving the agent without essential input semantics.
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 queries the Illegal Deforestation List of Pará (LDI) from an official source, with the verb 'consultar' and a specific resource. It naturally distinguishes itself from generic platform siblings (authenticate, connect, etc.) by its domain-specific purpose.
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 mentions payment via prepaid credit and LGPD responsibility, giving some context, but does not explicitly state when to use this tool versus alternatives or list exclusions. It implies usage for official deforestation data lookups but lacks clear prerequisites or situational guidance.
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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