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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive, but the description adds substantial behavioral context: the natural-language query patterns, the fast-path vs. grounded-pipeline routing, the exact verdict enum, the presence of pipeworx:// citations, and crucially the semantic distinction between could_not_verify (verification did not happen, with verification_error detail, never evidence) and unsupported (no source found). This goes far beyond the structured annotations.

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 a single dense paragraph, but it is front-loaded with query patterns and usage context, and every sentence adds value—especially the caller-facing caveats about could_not_verify and unsupported. It is longer than strictly necessary, but complexity of the tool justifies the length; a bulleted verdict list would improve scannability.

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 carries the full burden of explaining return values, and it does so thoroughly: verdict values, actual value with citation, reasoning, verification_error details, and the unsupported vs. could_not_verify distinction. It also covers routing, tolerance semantics, and tool replacement value, making it complete for this complex tool.

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% for the two parameters, so the baseline is 3. The description adds meaningful semantics on top: examples for the claim parameter, and for tolerance_pct it explains override behavior, default derivation ('implied by wording, capped at 5'), and a concrete use case ('set 1–2 for hallucination detection'). This enriches the schema without fully compensating for the absence of an output schema, but it is strong.

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 the verb 'validate' and a list of natural-language query patterns ('fact check', 'verify the claim that…', 'true or false'), making it unmistakably a claim-verification tool. It further distinguishes itself from siblings by specifying the two routing paths (SEC EDGAR/XBRL for company-financial claims, grounded pipeline for all else) and by noting it replaces multiple 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?

Explicitly states when to use: whenever the agent needs to check whether something a user said is factually correct. It also gives routing guidance for company-financial vs. other factual claims. However, it does not explicitly name alternative sibling tools or provide exclusion criteria (e.g., when to use ask_pipeworx_grounded instead), so it stops short of full alternative differentiation.

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