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

Annotations declare readOnlyHint=true, and the description adds valuable behavioral context beyond that: each figure carries provenance metadata, and foreign filers report in home currency requiring attention to the currency field. This goes beyond the annotation and helps the agent understand output nuances.

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 three sentences, each serving a distinct purpose: describing the output, giving usage guidance, and flagging a currency caveat. It is front-loaded and contains no redundant or filler content.

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 tool with one well-described parameter, an output schema, and read-only annotations, the description covers its purpose, usage, and a key data interpretation caveat. It is complete enough for an agent to correctly select and invoke the tool.

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 provides full coverage for the single ticker parameter, including an example and case-insensitivity note. The description does not add any additional parameter semantics, so the baseline of 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 that the tool returns 'Normalized fundamentals per fiscal year' and lists the metrics included, along with the unique provenance features (CIK, accession number, XBRL tag). This distinguishes it from sibling tools like get_intrinsic_value or screen_undervalued, which focus on different calculations or screening.

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 explicitly says 'Use when the user wants the underlying numbers or asks where a figure came from,' providing clear when-to-use guidance. It does not mention specific alternatives by name or provide when-not-to-use criteria, but the context is clear enough to guide tool selection.

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