fatos_contabeis

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full responsibility for behavioral disclosure. It explains that 'trimestral' changes granularity but not filtering, that APIs are optional for WEGE3, and how to fall back to an environment variable. It also clarifies the output includes period-level metrics with source codes. It does not describe the exact response structure or error behavior, but for a read-only data query, this is reasonable coverage.

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 well-structured: a short purpose paragraph followed by a bullet-style parameter list. It is concise, each sentence adds value, and the most important information (purpose) is front-loaded. There is no fluff or repetition, and the formatting aids scanning.

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 description covers the tool's purpose, parameters, and output concept (list of period metrics with source codes). However, it does not specify the exact JSON response structure, pagination, or error handling (e.g., invalid ticker). Since there is no output schema, a bit more detail on the return format would make it fully complete. Still, it is sufficient for an agent to invoke correctly and interpret the results at a high level.

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%, so the description must fully compensate. All three parameters are explained with examples, defaults, and business context: 'ticker' includes format and examples, 'trimestral' is thoroughly explained, and 'chave_api' covers optionality, default, and environment variable fallback. This far exceeds what a bare schema provides, making it excellent.

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 states a specific purpose: returning standardized accounting data for a B3 company with auditability (CVM account codes). It lists the specific metrics (receita, EBIT, etc.) and distinguishes from other tools by its focus on raw accounting figures with provenance. This is clear and specific, clearly apart from siblings like 'indicadores_anuais' or 'multiplos' which derive different metrics.

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 explains parameter behavior in detail (tickercase examples, trimestral granularity semantics, chave_api requirements), which aids correct invocation. However, it provides no explicit guidance on when to choose this tool over alternatives (e.g., when to use 'indicadores_anuais' instead). It only references 'listar_empresas' for ticker format, not for selection criteria. Thus, usage guidance is present but lacks comparative context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.