datasets_sec_companies_financials

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

With no annotations provided, the description fully discloses behavioral traits: it explains the return format (lines and ratios keys with examples), edge cases (unknown CIK returns empty series, most filers without ticker have no data), and enumerates the statement and period options. This provides complete visibility into the tool's operation and output structure.

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 well-organized paragraph that efficiently conveys all necessary information. It begins with the core purpose, then details return structure, edge cases, and key enumerations. No extraneous content exists. Minor improvement could be using bullet points for the key lists, but current structure is clear and 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?

The description adequately covers the tool's functionality given its complexity (6 parameters, no output schema, no annotations). It explains the return format in detail, edge cases, and parameter choices (statement/period enums). However, it does not mention pagination behavior (beyond limit/max points), and the ordering is noted but could be more explicit. Overall, it provides sufficient context for correct use.

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?

Although the input schema has 100% description coverage, the tool description adds significant value by explaining the meaning of returned data (e.g., `lines` keys like revenue, net_income; `ratios` keys like gross_margin) and elaborating on the statement and period enums (income, balance, cash_flow; annual, quarterly). This context goes beyond the schema's parameter descriptions and aids correct parameter usage.

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 purpose: 'Get a SEC company's financial-statement history.' It specifies the resource (financial-statement history) and distinguishes it from sibling tools like datasets_sec_companies_item or datasets_sec_companies_search by focusing on normalized financial statements (income, balance, cash flow). The action verb 'Get' is precise and unambiguous.

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 implicitly defines usage (to retrieve financial history) but does not explicitly state when to avoid this tool or which alternatives to use. It mentions behavior for unknown CIK (empty series) but lacks guidance on using other datasets_sec_companies tools for different data (e.g., company details or insider transactions). The context of use is clear but lacks exclusion criteria.

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