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

Beyond the readOnlyHint/openWorldHint annotations, the description discloses key behavioral traits: the two-path routing (structured vs. grounded), the verdict taxonomy, and critically the distinction between could_not_verify (check did not happen, carries verification_error) and unsupported (no source exists). It even warns callers not to treat could_not_verify as evidence for or against the claim — valuable operational detail.

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?

Although longer than a typical description, every sentence earns its place: trigger phrases, usage guidance, routing detail, return values, and failure-mode caveats are all packed in without redundancy. The description is front-loaded with the most important information (what the tool does) and then layers details in a logical order.

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 no output schema, the description thoroughly explains the return value (verdict types, actual value with citation, reasoning) and the failure semantics. It covers both the financial and non-financial paths, the tolerance behavior, and the distinction between could_not_verify and unsupported, making the tool fully actionable without additional documentation.

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 already documents both parameters with 100% coverage, so the baseline is 3. The description adds meaningful context beyond the schema: tolerance_pct overrides the claim-wording tolerance, defaults to implied wording capped at 5, and is recommended at 1–2 for hallucination detection. The claim examples also clarify the expected natural-language format.

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 identifies the tool as natural-language claim verification against authoritative sources, with specific trigger phrases ('fact check', 'verify the claim that…') and a distinct verdict output. It differentiates from sibling tools by explicitly describing the structured SEC EDGAR/XBRL fast path for financial claims and a grounded pipeline for all other claims, even 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 explicitly states when to use the tool ('Use whenever the agent needs to check whether something a user said is factually correct') and provides concrete example triggers. It does not explicitly name sibling tools or state when not to use it, but the routing logic (financial vs. other factual claims) gives strong contextual guidance.

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