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

Annotations already mark readOnly, openWorld, idempotent, and non-destructive. The description goes well beyond that by disclosing the refusal behavior with specific refusal_reason enums, the return shape including verbatim evidence, and the constraint that answers are extracted 'using ONLY what the tool result contains.' It also warns about the extra LLM-call cost, which is important operational behavior.

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 dense but every sentence earns its place: purpose, routing behavior, success/refusal output, use cases, and alternative recommendation. It is front-loaded with the most important differentiator ('Hallucination-resistant answer mode') and reads as a clear, compact specification rather than filler.

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?

With no output schema present, the description fully compensates by specifying the success return object and the complete refusal_reason enum. It also covers when to use the tool, when to prefer the sibling, and the cost implication. For a complex tool with source routing and refusal conditions, this is complete enough for an agent to select and invoke it correctly.

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 parameter 'question' is described as natural language with aliases. The description does not add much parameter-specific meaning beyond 'ask a question,' but the parameter is a single free-text string, so the schema already carries the necessary semantic weight. No gap is significant enough to raise or lower the baseline.

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 specific claim: 'Hallucination-resistant answer mode for high-stakes reads.' It then explains exactly what the tool does — same routing as ask_pipeworx, picks from 5,743 tools across 1,500 sources, fetches data, and extracts answers only from the tool result. This clearly differentiates it from its main sibling ask_pipeworx by emphasizing grounded extraction.

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 guidance is given: use 'whenever an answer will be quoted, cited, or acted on' and when the agent 'must not invent facts' — with concrete domains like financial verdicts, legal claims, medical lookups, and public statements. It also explicitly says to prefer ask_pipeworx for casual lookups, and mentions the cost tradeoff of one extra LLM call.

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