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

Annotations indicate a read-only, open-world, idempotent operation. The description adds crucial behavioral nuance: the distinction between 'could_not_verify' (check did not happen, must not be treated as evidence) and 'unsupported' (no source covers it), plus the verification_error structure. This goes well beyond the annotations and is essential for correct interpretation of results.

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?

Although the description is lengthy, every sentence carries substantive information: query examples, routing logic, return verdicts, and critical caller caveats. The structure front-loads purpose and usage, then details behaviors and dependencies, with no filler.

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, which it does thoroughly by listing all possible verdicts and their meanings, the citation format, and reasoning field. It also covers error handling (verification_error) and the two-tier routing, making it complete for a tool of this complexity.

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 already covers both parameters with descriptions (100% coverage), so baseline is 3. The description adds meaningful context for 'tolerance_pct' by explaining it overrides the claim's implied tolerance and how it's used for hallucination detection, and provides concrete examples for 'claim'. This elevates it above baseline.

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 a natural-language claim verification tool with a specific verb ('validate') and resource ('claim'). It lists example query phrases and explicitly distinguishes its function from siblings by focusing on fact-checking against authoritative sources.

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 states 'Use whenever the agent needs to check whether something a user said is factually correct' and explains the two distinct execution paths (structured SEC EDGAR for company-financial claims, grounded pipeline for other claims). It also notes it replaces multiple sequential calls, giving clear when-to-use context and efficiency rationale.

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