Dados cadastrais de uma pessoa física na Argentina a partir do DNI. Hospedado pela plataforma, sem credenciais, pague por consulta com crédito pré-pago. Consulta informação de ACESSO PÚBLICO em bases e fontes oficiais (a mesma disponível ao cidadão), não é dado privado nem sigiloso. O cliente é o controlador dos dados e responde pela finalidade legítima (LGPD).
| Name | Required | Description | Default |
|---|---|---|---|
| DNI | Yes | ||
| completo | No |
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?
Beyond the annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false), the description adds critical behavioral details: it is hosted by the platform, requires no credentials, incurs a per-query cost with prepaid credit, and queries only public official data. It also clarifies legal responsibility (client as data controller). These are valuable context beyond annotations and do not contradict 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 appropriately sized at three sentences. It leads with the core purpose, then adds payment and legal context. Every sentence contributes useful information, though the legal disclaimer could be considered slightly tangential. Overall, it is well-structured and not 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?
The tool has a small surface area (two parameters, no output schema). The description covers the data type, access method, credentials, payment, and legal responsibility. It does not describe the exact return structure or the effect of 'completo', but for a simple query tool this 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?
The schema has zero description coverage, so the description must compensate. It mentions 'DNI' as the input, which helps identify that parameter. However, the optional 'completo' boolean parameter is entirely unexplained, leaving the agent unclear about its semantics. This is a notable gap given the low schema coverage.
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's function: querying cadastral data of a person in Argentina using their DNI. It explicitly names the resource ('dados cadastrais de uma pessoa física na Argentina') and the input ('a partir do DNI'). It is also distinct from sibling tools (authenticate, connect, marketplace) which serve completely different purposes.
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 clear usage context: it is hosted by the platform, requires no credentials, and is paid per query with prepaid credit. It also specifies that the data is of public access and the client is responsible for legitimate purpose (LGPD). However, it does not explicitly mention alternative tools or provide exclusions, but the purpose is so distinct that the context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes, but `connect` and `toolkit_info` both report connection status, which could cause confusion. The domain tool `registro_pf_ar_consultar` is clearly unique.
Names mix single verbs (`authenticate`, `connect`), a noun (`marketplace`), and compound forms (`report_bug`, `show_version`), plus an inconsistent underscore-heavy domain name (`registro_pf_ar_consultar`). No clear naming pattern.
Seven tools is within a reasonable range, and the platform utilities earn their place alongside the single domain operation. The count is slightly high for such a narrow domain, but not excessive.
The domain surface is severely limited: only `registro_pf_ar_consultar` provides the core data lookup, with no other domain-related operations (e.g., different lookup keys, batch queries, or data explanations). The platform utilities are complete, but the server's stated purpose is the Argentina PF data, which is under-served.