Ficha de uma instituição financeira (cadastro, volumetria, reputação, reviews). 12 bancos tradicionais e digitais. Sem nome, lista as disponíveis.
| Name | Required | Description | Default |
|---|---|---|---|
| nome | No | Ex.: 'santander', 'nubank' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, so description carries full burden. It discloses return fields and behavior when no name is given. Does not mention side effects, authentication, or rate limits, but for a read-only query this is adequate.
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?
Two sentences, front-loaded with main purpose. No wasted words. Efficient and informative.
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?
For a simple tool with 1 parameter and no output schema, the description covers everything needed: what it does, what it returns, and behavior without input. 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% (1 parameter). Description adds value by clarifying that parameter is optional and what happens when omitted (lists available institutions). This goes beyond the schema's example values.
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?
Description clearly states it returns a profile of a financial institution with specific attributes (cadastro, volumetria, reputação, reviews). It distinguishes from siblings by being specific to financial institutions and mentions the set of 12 banks. No sibling overlap.
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?
Implied usage: call with nome for a specific institution, without to list available ones. But no explicit when-to-use or when-not-to-use compared to sibling tools. Siblings are quite different, so context aids selection.
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.
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.