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

Beyond the annotations (readOnly, idempotent, non-destructive), the description details return verdicts, the meaning of 'could_not_verify' (with verification_error and that it must not be shown as evidence), and the unsupported state. This enriches the caller's understanding of edge cases, which annotations alone do not provide.

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 about 200 words but every sentence adds value: trigger phrases, routing logic, return value, and error semantics are all packed in without redundancy. The content is front-loaded with the core purpose and usage, making it easy to scan despite its length.

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?

For a complex tool with no output schema, the description is exceptionally complete: it enumerates the six possible verdicts, explains the structured vs. grounded pipeline, states that the answer includes a pipeworx:// citation and reasoning, and addresses both error and unsupported cases. No critical operational detail is missing.

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?

Even though schema coverage is 100%, the description adds significant meaning: it explains that tolerance_pct overrides the tolerance implied by the claim wording, shows the allowed range (0.5–50), and suggests setting 1–2 for hallucination detection. The claim parameter is also illustrated with concrete examples, going beyond the schema's simple type/description.

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 names a specific verb ('validate' / 'verify') and resource ('natural-language claim verification against authoritative sources'), and immediately distinguishes its scope by giving trigger phrases. It also separates two execution paths (SEC EDGAR for company-financial claims vs. a grounded pipeline for everything else), making it clearly distinct from sibling tools like ask_pipeworx_grounded or compare_entities.

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?

Explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct,' and gives concrete examples of when to use it over alternatives (e.g., company-financial claims vs. other claims). It also explains that it replaces multiple sequential calls, reinforcing when to prefer this single tool over composing several others.

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