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

Beyond the annotations (readOnly, idempotent, openWorld, non-destructive), the description discloses crucial behavioral traits: the two distinct execution paths (SEC EDGAR fast path vs grounded pipeline), the meaning of each verdict, and especially the warning that could_not_verify means the check did not happen (verification_error) and must not be used as evidence. This goes far beyond the annotation hints and prevents misinvocation.

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 every sentence contributes: it opens with user-phrase triggers, defines the scope, explains routing, enumerates outputs, provides critical caller warnings, and ends with an efficiency justification. It is well-structured and front-loaded, with no filler or redundancy.

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?

Even without an output schema, the description fully specifies what callers will receive: verdict enum, actual value with a pipeworx:// citation, reasoning, and error structure for could_not_verify. It also addresses edge cases (unsupported vs could_not_verify) and the two data-source paths. For a tool of this complexity, the description is exceptionally 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?

The schema already describes both parameters, and the description adds valuable context: the claim parameter examples show expected formats, and tolerance_pct is explained with its role in overriding the implied tolerance, a recommendation for hallucination detection, and its default (capped at 5). This exceeds the baseline for 100% schema coverage by adding operational guidance.

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 a specific verb ('verify') and resource ('natural-language claim verification against authoritative sources'). It provides concrete trigger phrases ('fact check', 'confirm or refute'), describes the two routing paths (SEC EDGAR + XBRL for company-financial claims, grounded pipeline for other claims), and lists the exact verdict values returned. This distinguishes it from sibling tools like ask_pipeworx_grounded or deep_research by focusing on claim adjudication with a defined outcome.

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: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also clarifies the scope by distinguishing company-financial claims from any other factual claim and explains the fallback behavior. The note that it 'Replaces 4–6 sequential calls' gives a clear reason to prefer this tool over composing multiple primitive calls.

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