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

Beyond the readOnly/openWorld/idempotent annotations, the description provides critical behavioral context: 'could_not_verify means the check did not happen... it is NOT evidence for or against the claim, and must not be shown as one.' It also clarifies 'unsupported' semantics and the internal fallback pipeline. This is exactly the type of nuance an agent needs to interpret results correctly.

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 front-loaded with trigger phrases and a clear use-case. It covers multiple aspects (inputs, outputs, error semantics, replacing sequential calls) without unnecessary fluff. The opening example list could be trimmed, but it serves as intent-matching aid for the model.

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 fully explains return values: verdict enum, actual value with citation, and reasoning. It also covers both domain-specific paths and error cases. The note about replacing 4–6 sequential calls adds operational context. Given the tool's complexity, the description is complete for safe and correct invocation.

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 covers 100% of parameters with clear descriptions, so the baseline is 3. The tool description adds some value by explaining how tolerance_pct overrides claim-wording tolerance and its role in grading (e.g., hallucination detection), but this largely reinforces schema information. No major gaps exist, but no significant added meaning beyond the schema either.

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 explicitly states the tool's function: 'natural-language claim verification against authoritative sources.' It provides example user phrasings, details the two processing paths (SEC EDGAR for financial claims and grounded pipeline for others), and distinguishes itself from siblings by focusing on verification with verdicts. This is a specific verb+resource with clear scope.

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 clearly states when to use it: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the domain split (financial vs. other claims). However, it does not explicitly name alternatives or state when NOT to use this tool, so it falls short of the highest bar.

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