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

The annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral context by explaining the two execution paths (SEC EDGAR/XBRL vs. grounded pipeline), the return verdict categories, and the critical distinction between 'could_not_verify' (pipeline failure) and 'unsupported' (no source found). This goes well beyond the annotations and clarifies failure semantics.

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 dense but every sentence contributes. It is front-loaded with trigger phrases and usage guidance, then efficiently covers routing, return values, and caveats. Although longer than the high-scoring get_calls example, the complexity of the tool justifies the length, and there is no redundancy or 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?

Given no output schema, the description fully covers return values (verdict, actual value with citation, reasoning), error semantics (verification_error, could_not_verify vs. unsupported), and routing logic. It also explains why the tool exists (replaces sequential calls) and how the two paths work, leaving the agent well-equipped to invoke and interpret results.

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%, so the baseline is 3. The description adds important meaning beyond the schema: it explains how tolerance_pct overrides the tolerance implied by claim wording, suggests setting 1–2 for hallucination detection, and states the default (implied by wording, capped at 5). The claim parameter is given concrete examples and its natural-language nature is reinforced.

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 trigger phrases ('Is it true that…', 'fact check', 'verify the claim that…') and clearly defines the tool as natural-language claim verification against authoritative sources. It also distinguishes itself from sibling tools by spanning both structured SEC/XBRL fast-path for company financials and a grounded pipeline for any other factual claim, and by stating 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?

The description explicitly states when to use the tool: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also provides clear routing guidance (company-financial vs. any other claim) and names the alternative it replaces (4–6 sequential calls), giving the agent concrete usage direction.

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