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

The annotations already declare read-only, open-world, and idempotent behavior. The description adds significant transparency beyond that: it details the two pipelines, discloses failure modes (could_not_verify with verification_error), clarifies that could_not_verify is NOT evidence, and explains the meaning of unsupported. This directly prevents misuse and goes far beyond the structured 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 lengthy but well-structured: it opens with example phrases, states the core usage, explains pipelines, return values, and important caveats. Every section serves a purpose given the tool's complexity. While not minimal, it avoids fluff and is appropriately sized for the range of behaviors it must cover.

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 fully covers return values (verdicts, citation, reasoning), explains error semantics, and outlines the routing logic. It also accounts for the tool's safety profile via annotations. The description is complete enough for an agent to invoke the tool correctly and interpret results without ambiguity.

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 description coverage is 100%, so the schema already fully documents both 'claim' and 'tolerance_pct' with examples and default behavior. The description does not add any parameter-specific meaning; it mentions 'exact percent-delta math' but does not elaborate on the parameters themselves. Baseline 3 is appropriate because the schema does the heavy lifting.

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's purpose: natural-language claim verification against authoritative sources, returning verdicts. It uses specific verbs like 'check', 'verify', and 'confirm or refute', and distinguishes itself from sibling tools by focusing on fact-checking with structured verdicts rather than general search or research. The examples of invocation phrases make the purpose immediately actionable.

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 it: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the two verification paths (company-financial vs. other claims) and notes that it 'replaces 4–6 sequential calls,' implying a preference over multi-step alternatives. However, it does not explicitly name alternative tools or state when not to use it, so it falls short of the highest bar.

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