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

Far beyond the annotations (readOnlyHint, openWorldHint, idempotentHint), the description discloses the full success return contract ({answer, evidence verbatim quote, confidence, source, fetched_at, refusal_reason:null}), the explicit refusal contract with five enumerated refusal_reason values, the grounding constraint ('EXTRACTS the answer using ONLY what the tool result contains'), and the extra LLM-call cost. No contradiction with annotations; readOnlyHint=true is consistent with a read-and-answer mode.

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?

Four sentences, each carrying a distinct payload: mode definition and mechanism, success return contract, refusal contract, and usage/cost guidance. The key differentiator is front-loaded, and the refusal_reason enum earns its place since the tool has no output schema to document it.

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 covers the return shapes for both success and refusal, explains the underlying mechanism, the cost tradeoff, and when to prefer the sibling tool. The only unstated details (confidence scoring semantics, source format) are minor, and the annotations already carry the safety/open-world profile.

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% — the input schema already fully documents the natural-language question and all five aliases ('Accepts query, q, prompt, text, input as aliases'). The description adds process context (routing, argument filling, extraction) but no additional parameter-level semantics, so the high-coverage baseline of 3 applies.

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 mode ('Hallucination-resistant answer mode for high-stakes reads') with a precise verb-resource contract: route the question across 5,908 tools and 1540 sources, fetch data, then extract the answer using only the tool result. It explicitly distinguishes itself from the sibling ask_pipeworx ('Same routing as ask_pipeworx'... 'prefer ask_pipeworx for casual lookups'), so an agent can tell the two apart without opening schemas.

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?

Explicit when-to-use guidance is present: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' with concrete example domains (financial verdicts, legal claims, medical lookups, public statements). It also states the when-not-to-use case ('prefer ask_pipeworx for casual lookups') and gives the rationale — one extra LLM call — leaving nothing to inference.

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