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

Annotations already declare read-only, open-world, and idempotent behavior, so the bar is lower; the description still adds substantial context that goes beyond annotations. It explains the two processing pipelines, the meaning of each verdict status, and crucially distinguishes 'could_not_verify' (check did not happen, not evidence) from 'unsupported' (no source exists), plus the verification_error field. This is exactly the kind of behavioral nuance that agents need.

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 and well-organized: trigger phrases, when-to-use, internal routing, return contents, a critical caller warning, and efficiency claim. Every sentence earns its place, and the structure front-loads the most useful detection cues. It is longer than the TDQS 4.3 example, but the complexity of the tool and the need to explain error semantics justify the length. A score of 4 recognizes the excellent structure without penalizing the necessary detail.

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?

Despite having no output schema, the description fully covers the return value: verdict enum, actual value with pipeworx:// citation, and reasoning. It also explains the error semantics (could_not_verify vs. unsupported), the tolerance override, and the routing logic. Given the tool's two-path complexity and the absence of an output schema, the description is exceptionally complete and leaves no major ambiguity.

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 baseline is 3. The description adds meaningful guidance beyond the schema for tolerance_pct: 'Overrides the tolerance implied by the claim wording — set 1–2 for hallucination detection where any material error must be refuted. Default: implied by wording, capped at 5.' The claim parameter also receives illustrative examples in the schema, which the description reinforces. This extra context justifies a score above baseline, though not the top as no additional parameter-level semantics are added.

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 verb and resource: 'natural-language claim verification against authoritative sources.' It is prefixed with concrete trigger phrases (""Is it true that…" / "fact check" / "verify the claim that…"") and differentiates from sibling tools by describing its distinct internal routing (SEC EDGAR fast path vs. grounded pipeline) and its single-call replacement of 4–6 sequential steps.

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?

Explicit usage guidance is given: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also provides an internal decision rule (company-financial claims vs. any other factual claim) and notes that it replaces multiple sequential calls, which implies when to choose this over lower-level alternatives.

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