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

Beyond the readOnly/openWorld annotations, the description adds critical behavioral context: the two-track routing, the specific verdict set, and the important caveat that could_not_verify means the check did not happen and must not be shown as evidence. It also clarifies the difference between could_not_verify and unsupported, which is not implied by the 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 long but tightly structured: examples lead in, then usage trigger, routing logic, return summary, and a clear caller warning. Every sentence carries distinct, non-redundant information, and the critical warning is highlighted with IMPORTANT.

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?

This is a complex tool with two processing paths, a rich verdict set, and error semantics. Without an output schema, the description fully compensates by explaining verdict meanings, citation format, and the behavior of could_not_verify versus unsupported. It gives an agent everything needed to select and invoke correctly.

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, but the description adds meaningful semantics beyond it. For tolerance_pct it explains overrides the claim-implied tolerance, recommends 1–2 for hallucination detection, and notes the default cap of 5. Claim examples also illustrate the expected input format, which the schema only partially conveys.

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 natural-language examples ("Is it true that…", "fact check") and states it performs "natural-language claim verification against authoritative sources". It clearly distinguishes this from sibling query tools by covering both company-financial claims via the structured SEC EDGAR path and any other factual claim via the grounded pipeline.

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?

"Use whenever the agent needs to check whether something a user said is factually correct" gives an explicit trigger condition. It also explains the routing rule for financial vs. other claims and notes it replaces 4–6 sequential calls, making it clear this is the consolidated tool for claim verification without needing alternatives.

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