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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description goes well beyond by detailing the return verdicts, the fast path for company-financial claims, the fallthrough to a grounded pipeline, and the crucial semantic distinction between 'could_not_verify' (check did not happen) and 'unsupported' (no source found). It also warns callers not to treat 'could_not_verify' as evidence, which is critical behavioral context.

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 highly structured: trigger phrases, use case, sub-path explanations, return values, and an 'IMPORTANT' callout. While slightly verbose, every sentence carries meaningful information and the critical warnings justify the length. It could be tightened, but the organization makes it easy to parse.

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?

With no output schema, the description compensates fully by explaining the set of possible verdicts, the return of a grounded/structured value with a citation, reasoning, and error semantics. It also covers the two verification routes and the confidence/edge cases. For a tool with two parameters and no output schema, this is a complete and self-sufficient 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?

The schema already fully describes both parameters (100% coverage), but the description adds substantial value: it clarifies the tolerance_pct default ('implied by wording, capped at 5'), shows an example override for hallucination detection, and helps the agent decide when to set it. The claim parameter is illustrated with realistic examples, exceeding what the schema alone provides.

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 explicit trigger phrases ('Is it true that…', 'fact check', 'verify the claim that…') and states the core function: natural-language claim verification against authoritative sources. It distinguishes itself from siblings by noting it 'Replaces 4–6 sequential calls' and describes two distinct verification paths with exact detail, making the tool's scope and purpose unmistakable.

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?

Directly states when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It further gives guidance on claiming company-financial vs. other claims and explains the meaning of 'could_not_verify' versus 'unsupported', including that the former must not be shown as evidence. This is explicit, actionable usage direction.

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