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

Despite annotations already marking readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, the description adds crucial behavioral nuance: it explains the meaning of 'could_not_verify' (check did not happen, carries verification_error, must NOT be shown as evidence) versus 'unsupported' (no source covers it). This is exactly the kind of edge-case behavior that would be invisible without explicit disclosure and prevents a caller from misusing the result.

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 dense yet well-structured, with an opening trigger list, a clear use-case sentence, a pipeline overview with routing rules, a verdict list, and a critical caller warning. Every sentence adds operational value; the IMPORTANT section is appropriately emphasized. Length is justified by the tool's complexity.

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 tool handles two distinct verification pipelines, multiple verdict classifications, and has ambiguous failure modes. The description covers routing, evidence citation format, verdict semantics, and the critical could_not_verify vs unsupported distinction. With a rich input schema and clear annotations, there are no major gaps that would leave an agent guessing about return behavior or usage constraints.

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%, so the schema already documents both parameters. The description adds value by explaining what the default tolerance behavior is ('implied by wording, capped at 5') and when to override it (hallucination detection with 1–2). It also orients the 'claim' parameter with examples in the schema, so the description's added practical guidance is meaningful but not exhaustive.

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 opens with multiple natural-language trigger phrases ('Is it true that…', 'fact check', 'verify the claim that…') and states a specific verb+resource: 'natural-language claim verification against authoritative sources.' It clearly distinguishes this from sibling research tools by focusing on verifying a claim's truthfulness, not general research or entity lookup.

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 whenever the agent needs to check whether something a user said is factually correct.' It further differentiates two paths — structured SEC EDGAR for company-financial claims and a grounded pipeline for any other factual claim — and notes that it 'replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).' This gives the agent clear when-to-use and what-alternatives-it-replaces context.

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