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

The annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the description adds significant behavioral context beyond that: the meaning of each verdict (confirmed, refuted, etc.), the critical distinction between 'could_not_verify' and 'unsupported', and the fact that it returns verbatim evidence with pipeworx:// citations. This is exactly the kind of disclosure that helps an agent trust and interpret results.

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?

Although lengthy, every sentence serves a purpose: examples for recognition, routing rules, verdict list, caller warnings, and a note about replacing 4–6 sequential calls. The content is front-loaded and well-structured, flowing from what it does to how it behaves to what callers must know.

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 pipelines, multiple verdicts, error handling) and the absence of an output schema, the description covers all essential aspects: return values, citation format, reasoning, and the semantic distinction between could_not_verify and unsupported. It is complete enough for an agent to use the tool correctly and interpret its output appropriately.

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 coverage is 100% with descriptive parameter docs, so baseline is 3. The description goes further by explaining how tolerance_pct interacts with claim wording and suggesting values (1–2) for hallucination detection, and by giving concrete example claims. This adds meaning beyond the schema without being redundant.

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 concrete example phrasings and a clear verb+resource: 'natural-language claim verification against authoritative sources.' It explicitly distinguishes two routing paths (SEC EDGAR/XBRL fast path for company-financial claims vs. grounded pipeline for all others), which differentiates it from sibling tools like search or ask_pipeworx_grounded.

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') and explains the routing logic for claim types. It does not explicitly name alternative tools or state when not to use it, but the context is clear enough for an agent to select it appropriately.

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