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

The description adds critical behavioral context beyond the annotations: it explains the two routing paths (structured SEC EDGAR for financial claims, grounded pipeline for others), describes return verdicts, and importantly defines the semantics of could_not_verify (carries verification_error{stage,detail}, not evidence) and unsupported (no source found). This is valuable nuance that annotations alone do not provide, and there is no contradiction with the readOnly/openWorld/idempotent hints.

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 long but well-structured: leading examples, clear use-case statement, pipeline breakdown, return value summary, and error semantics. Every sentence adds necessary information, and the use of dashes and parentheses packs detail efficiently. It is not overly terse (which would lose nuance) nor rambling (it stays focused), earning a high but not perfect score because some sentences could be tightened.

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 having no output schema, the description thoroughly covers return values (verdict, value, citation, reasoning), error states, and routing behavior. It gives enough context for an agent to invoke the tool correctly and interpret results, including guidance on how to handle ambiguous verdicts. The description is complete for a tool of this complexity, even without structured output 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?

The input schema already describes both parameters with 100% coverage. The description adds extra meaning by giving a concrete example for the claim parameter and by explaining that tolerance_pct overrides the wording implied tolerance and is useful for hallucination detection when set to 1–2. This goes beyond the schema's basic parameter descriptions, though the schema already carries much of the load.

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 natural-language examples ('Is it true that…', 'fact check') and states the tool performs 'natural-language claim verification against authoritative sources.' It clearly distinguishes itself from sibling tools like ask_pipeworx_grounded and deep_research by specifying the SEC EDGAR fast path for financial claims and the grounded pipeline for all other factual claims, and by noting it replaces multiple sequential calls.

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.' It also gives guidance on when to set tolerance_pct (1–2 for hallucination detection) and clarifies that could_not_verify means the check did not happen and must not be shown as evidence. This provides clear when-to-use context and identifies what makes this tool different from general research tools.

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