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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, and the description adds substantial behavioral context beyond that: the dual-path execution (structured SEC EDGAR + XBRL vs grounded pipeline), the full verdict enum (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), and the critical distinction that could_not_verify means the check did not happen and must not be shown as evidence, including the verification_error{stage,detail} structure. It also explains tolerance cap behavior.

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 long but every sentence earns its place: trigger phrases, conditional paths, return values, error-handling guidance, and a value proposition ('Replaces 4–6 sequential calls'). It is front-loaded with the trigger phrases, then flows logically through behavior, return structure, and caveats, making it dense yet well-organized with no wasted words.

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 the tool's complexity (two execution paths, six verdict states, error details) and the absence of an output schema, the description fully covers what a caller needs: how to invoke it, what it returns (verdict, actual value with citation, reasoning), how to interpret edge cases (could_not_verify vs unsupported), and how to tune tolerance. There are no significant gaps for an agent to misuse the tool.

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?

Both parameters are fully described in the schema (100% coverage), so baseline is 3. The description adds meaningful details beyond the schema: tolerance_pct range (0.5–50), default implied by wording and capped at 5, and the use case for hallucination detection (set 1–2 for any material error). The claim parameter is well-covered by schema examples, so the description's added value for tolerance semantics justifies a 4.

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 natural-language trigger phrases ('Is it true that…', 'fact check', 'verify the claim'), then states the specific verb+resource: 'natural-language claim verification against authoritative sources.' It clearly distinguishes between company-financial claims (SEC EDGAR + XBRL path) and other factual claims (grounded pipeline), and notes it replaces 4–6 sequential calls, differentiating it from sibling tools like ask_pipeworx_grounded and deep_research.

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?

It explicitly states when to use the tool: 'Use whenever the agent needs to check whether something a user said is factually correct.' It provides context on the two execution paths and gives crucial caller guidance about interpreting could_not_verify vs unsupported. However, it doesn't explicitly name alternative tools or state when not to use this tool, so it lacks explicit exclusions but has clear context.

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