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

The description adds substantial behavioral context beyond the annotations: the two execution paths, the full set of possible verdicts, and the critical caveat that could_not_verify means the check did not happen and must not be treated as evidence for or against the claim. It also explains the meaning of unsupported. This goes well beyond the readOnlyHint/openWorldHint 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 information-dense. It is well-structured, starting with trigger phrases, then use cases, then routing, then return values, and then error semantics. Every sentence contributes useful details, though it could be tightened slightly without losing critical caveats.

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 lacking an output schema, the description covers the return values (verdicts, actual value with citation, reasoning) and distinguishes the two failure cases (could_not_verify vs unsupported). Combined with the annotations, this gives an agent everything it needs to select, invoke, and interpret the tool's results 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 both parameters, but the description enriches the semantics: claim gets natural-language examples, and tolerance_pct is explained as overriding the implied tolerance and being useful for hallucination detection. This provides meaningful guidance on how to set and interpret the parameter beyond the schema's basic description.

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 identifies the tool as natural-language claim verification against authoritative sources, with explicit examples of trigger phrases like 'fact check' and 'verify the claim that'. It also distinguishes itself from sibling research tools by focusing on returning verdicts like confirmed/refuted, and by noting it replaces 4–6 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?

It explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct' and provides detailed routing guidance (SEC EDGAR for company-financial claims, grounded pipeline for any other factual claim). However, it does not name alternative tools or explicitly state when not to use it, so it stops short of a full when/when-not/alternatives breakdown.

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