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

The description adds significant behavioral context beyond the annotations: it explains the exact meaning of 'could_not_verify' (the check did not happen and must not be treated as evidence), 'unsupported' (no source covered), and the structured vs. grounded pipeline routing. It also discloses the return format (verdict, actual value, citation, reasoning). This goes far beyond the read-only/idempotent annotation hints and proactively prevents misuse.

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 front-loaded with trigger phrases and organized logically: it states the purpose, details the two pipelines, lists the output, and gives important caller caveats. Although it is longer than typical descriptions, every sentence carries unique information—the trigger examples, tolerance guidance, and error semantics all earn their place. No padding detected.

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 complexity of the tool (two processing paths, six verdicts, error handling) and the absence of an output schema, the description thoroughly covers what the agent needs to invoke the tool correctly and interpret results. It explicitly explains the distinction between 'could_not_verify' and 'unsupported', states the citation mechanism, and notes that it consolidates multiple sequential calls. Nothing critical appears 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?

Schema coverage is 100% and both parameters have descriptions, so the schema already documents them. The description adds extra semantic value, especially for tolerance_pct, by explaining how the implied tolerance default works and suggesting 1–2% for hallucination detection. This guidance is useful beyond the schema's basic 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 clearly defines the tool's function as natural-language claim verification against authoritative sources, with specific verbs like 'verify' and 'check'. It includes trigger phrase examples and describes the two processing paths (SEC EDGAR for company-financial claims, grounded pipeline for others). However, it does not explicitly distinguish itself from sibling tools such as ask_pipeworx_grounded or deep_research, so it lacks explicit sibling differentiation.

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 states a clear usage context: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the internal routing for company-financial vs. other claims and mentions it replaces 4–6 sequential calls, implying efficiency benefits. However, it does not explicitly name alternatives or state when NOT to use this tool, leaving room for ambiguity with sibling tools.

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