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

The description goes far beyond the readOnlyHint and idempotentHint annotations by disclosing nuanced behavior: the distinction between 'could_not_verify' (verification did not happen, not evidence) and 'unsupported' (no source exists), the two routing paths, the return format with citations, and the tolerance math. This is critical behavioral context that the annotations do not provide, and it is consistent with 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 longer than average but well-structured: it leads with natural-language triggers, then explains the purpose, routing, output, and a clearly flagged 'IMPORTANT' caller note. Every sentence adds value, and the formatting (dashes, capitalization) improves scannability. It could be trimmed slightly, but it is not bloated.

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?

Since there is no output schema, the description fully specifies the return value: verdict types, actual value with citation, reasoning, and the meaning of two ambiguous verdicts. It also explains the two execution paths and covers the tool's scope. For a complex verification tool with nuanced outcomes, this is highly complete.

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 does not add extra meaning beyond the schema; it mentions 'exact percent-delta math' and 'tolerance implied by the claim wording' which are already captured in the parameter descriptions. The schema itself fully documents both parameters, so no additional semantics are needed.

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 states the tool's function: 'natural-language claim verification against authoritative sources.' It names specific verbs ('verify', 'confirm or refute', 'true or false') and the resource (claims), plus distinguishes two distinct processing paths (SEC EDGAR for company-financial claims, grounded pipeline for all others). This is a specific, actionable purpose that stands apart from generic research tools.

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 says 'Use whenever the agent needs to check whether something a user said is factually correct,' providing a clear trigger. It also explains routing for different claim types and notes that it 'Replaces 4–6 sequential calls,' implying efficiency. However, it does not explicitly name alternative tools to use instead for different scenarios, so it lacks the when-not-to-use exclusions that would earn a 5.

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