Fichas completas de até 10 personas por id (atributos + Grounding com fatos/fontes + História). Use para dar voz às personas com ancoragem nos dados. IMPORTANTE: fatos marcados composicao são P(público | métrica), o inverso de propensão — não os leia como probabilidade da persona; fatos referencia não são proporção de pessoas.
| Name | Required | Description | Default |
|---|---|---|---|
| ids | Yes | Ex.: ['PERS_023', 'PERS_041'] |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses crucial interpretation details about _composicao_ and _referencia_ facts, but does not mention non-destructive nature, rate limits, or authentication needs. The description adds value beyond annotations (which are absent) but is incomplete.
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 two sentences plus a critical note, all front-loaded. Every sentence adds value: main purpose, then interpretation guidance. No filler or redundancy.
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 a single parameter with full schema coverage, no output schema, and sibling context, the description adequately explains the tool's output (complete cards with attributes, grounding, history). However, lacking explicit return structure details keeps it from being fully 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 coverage is 100% with the 'ids' parameter fully described in the schema (including example). The description does not add additional semantic meaning about the parameter beyond what the schema provides, hence baseline 3 is appropriate.
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 it retrieves complete persona cards by ID, listing attributes, grounding with facts/sources, and history. The verb 'get' and resource 'personas' are specific, and the mention of 'até 10 personas' distinguishes it from sibling tools like 'filtrar_personas' which filter lists.
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 use when full persona profiles are needed by ID, but lacks explicit when-not-to-use or alternative comparisons. The important note about interpreting facts provides context for correct usage, but no exclusions are stated.
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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Each tool has a clearly distinct purpose: overview, fact search, persona filtering, persona details, sampling, survey preparation, confidence assessment, distributions, institution info, and voice examples. No two tools overlap significantly.
Most tools follow a verb_noun pattern, but some use English prefixes (get_) while others use Portuguese verbs (avaliar_, buscar_). This inconsistency could cause confusion, though the names are still readable.
10 tools is appropriate for the synthetic panel domain. Each tool serves a distinct function without redundancy, and the count is neither too few nor too many.
The tool set covers the full workflow: overview, question classification, fact retrieval, persona filtering and display, sampling, survey preparation, distribution tables, institution lookup, and voice examples. No obvious gaps for the stated purpose.