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

Annotations already declare read-only, open-world, idempotent, and non-destructive traits. The description goes beyond this by explaining the dual execution paths, the precise meaning of verdicts (especially the crucial caveat that 'could_not_verify' means the check did not happen and is NOT evidence), and that unsupported means no source was found. It also discloses that the tool automates multiple sequential steps.

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 moderately long (~150 words) but efficiently packs trigger phrases, core functionality, the two-path routing, return values, caveats, and a value proposition. It is front-loaded with examples and prioritizes the most actionable information. Slightly verbose, but every sentence contributes meaning.

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 correctly takes responsibility for explaining return values: it enumerates the six verdicts, mentions the 'grounded or structured actual value with pipeworx:// citation', and details the verification_error structure. It also covers the two execution paths and the distinction between 'could_not_verify' and 'unsupported', making the tool's behavior fully comprehensible for a complex claim-verification task.

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%: both 'claim' and 'tolerance_pct' are documented in the input schema. The description does not add extra parameter-level semantics—it mentions the claim pattern in the trigger phrases (e.g., 'did X really…') but does not discuss tolerance_pct explicitly. It is adequate but not additive.

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 defines the tool as claim verification against authoritative sources, with explicit verb ('verify', 'validate') and a specific scope (fact-checking natural-language claims). It distinguishes itself from siblings by detailing two concrete pipelines (SEC EDGAR for company-financial claims, grounded pipeline for others) 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?

The description states precisely when to use this tool ('Use whenever the agent needs to check whether something a user said is factually correct') and provides a decision tree (company-financial vs. any other claim). It also implies when not to use it (e.g., non-factual queries) and offers a clear alternative via the grounded pipeline, effectively guiding the caller.

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