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

The description adds substantial behavioral context beyond the annotations: it explains the meaning of could_not_verify (check did not happen, not evidence) versus unsupported (no source found), and details the routing behavior for company-financial versus other claims. It also discloses the return composition (verdict, actual value, citation, reasoning) and the verification_error field. This goes well beyond the readOnly/openWorld/idempotent hints, and no contradiction with annotations exists.

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-organized: trigger phrases, core purpose, routing behavior, return details, important caller caveat, and value proposition. Every sentence adds information, with no filler. The critical note about could_not_verify is clearly highlighted with 'IMPORTANT for callers.' The 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?

Given the tool's complexity, the description is remarkably complete. It covers when to use, what happens for different claim types, the verdict schema (including edge cases like could_not_verify and unsupported), and why it replaces multiple sequential calls. No output schema exists, so the description appropriately explains the return value structure inline. Annotations and schema are fully leveraged, leaving no major gaps.

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 describes both parameters fully (100% coverage), so baseline is 3. The description adds extra meaning: tolerance_pct overrides the tolerance implied by the claim wording, defaults to 5 cap, and is recommended for hallucination detection. The claim parameter examples in the schema are complemented by the description's explanation of how claims are routed. This added context pushes the score to 4.

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 concrete trigger phrases (""Is it true that…" / "fact check" / "verify the claim that…") and clearly states the tool performs natural-language claim verification against authoritative sources. It distinguishes itself by describing two specific pipelines (SEC EDGAR fast path for company-financial claims, grounded pipeline for all others), which sets it apart from sibling tools like ask_pipeworx or deep_research.

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," giving a clear when-to-use signal. It also states it "Replaces 4–6 sequential calls," implying it is the consolidated alternative to a multi-step workflow. However, it does not name specific sibling tools to avoid or provide explicit when-not-to-use scenarios, so it falls short of a 5.

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