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

This description goes far beyond the annotations. It explains critical failure semantics: 'could_not_verify means the check did not happen... it is NOT evidence for or against the claim, and must not be shown as one.' It also details the meaning of 'unsupported' (we looked and cover no source), the verdict types returned, and the inclusion of a pipeworx:// citation. The readOnlyHint and idempotentHint are consistent with the description — it performs no mutation. The description adds rich context about errors and return behavior that annotations do not cover.

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 but every sentence adds necessary value: trigger phrases, routing logic, verdict list, failure semantics, and the performance benefit. It is front-loaded with the most actionable information ('Use whenever...') and structured naturally from purpose to behavior to caveats. There is no filler or tautology.

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?

Despite lacking an output schema, the description fully describes the return payload (verdict, actual value with citation, reasoning) and clarifies the meaning of each error state (could_not_verify, unsupported). It also explains the two distinct pipelines and their scope (financial vs. other). Given the tool's complexity, this is a complete and self-contained description that would allow an agent to invoke it correctly without further documentation.

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%, so the baseline is 3. The description repeats the tolerance_pct semantics already present in the schema (e.g., the default, the 0.5–50 range, and the hallucination-detection use case) without adding new parameter-level detail. The claim parameter is also documented in the schema with examples. The description does contextualize parameters within the overall flow, but no additional meaning is added beyond what the schema already provides.

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 a clear, specific verb-object pair ('validate claim') and immediately defines the tool's role as natural-language claim verification against authoritative sources. It distinguishes itself from sibling tools like ask_pipeworx_grounded and deep_research by focusing on issuing a verdict (confirmed, refuted, etc.) and by explicitly describing the two processing paths (SEC EDGAR for financial claims, grounded pipeline for everything else).

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 provides a direct usage trigger: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also outlines the routing logic (which claims go to the structured path vs. grounded pipeline) and notes that the tool replaces 4–6 sequential calls, giving integration context. It does not explicitly name alternative tools or state when NOT to use it (e.g., for subjective or non-factual questions), so it lacks an explicit exclusion clause but still offers strong guidance.

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