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

Annotations already declare readOnly, openWorld, idempotent, and safe traits. The description goes well beyond annotations by explaining two execution paths (SEC EDGAR structured vs. grounded pipeline), defining the verdict enum, and crucially distinguishing 'could_not_verify' (internal failure, not evidence) from 'unsupported' (no source). This is critical behavioral context for correct interpretation.

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 long but every sentence earns its place. It opens with example phrasings, explains routing logic, lists return values, and ends with a critical caveat about error semantics. The structure is logical and front-loaded, with no filler or repetition.

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 the tool's complexity and absence of an output schema, the description fully compensates: it spells out return values (verdict, cited value, reasoning), enumerates possible verdicts, explains error behavior, and even mentions what the tool replaces. Annotations cover safety, and the two parameters are fully documented in both schema and description.

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 good descriptions for both parameters. The description adds extra semantics: it explains that tolerance_pct overrides the tolerance implied by claim wording, gives specific guidance for hallucination detection (1–2), and illustrates the claim parameter with concrete examples. This goes beyond the schema's baseline.

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 function: natural-language claim verification against authoritative sources. It uses specific verbs like 'fact check,' 'verify,' and 'confirm or refute,' and even provides example phrasings. It distinguishes itself from siblings by focusing on claim verification and explicitly noting it replaces 4–6 sequential calls.

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 gives explicit guidance on when to use the tool ('Use whenever the agent needs to check whether something a user said is factually correct') and explains routing for different claim types (company-financial vs. other). It does not name alternative sibling tools or provide exclusions, but the context is clear and the 'Replaces 4–6 sequential calls' line frames it as a consolidated alternative.

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