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

The description fully discloses the success output shape, the exact refusal_reason enum values, and the guarantee that an answer is extracted only from what the tool result contains. It also reveals the additional LLM-call cost and explicit refusal behavior, going well beyond the readOnly/openWorld/idempotent/non-destructive annotations. There is no contradiction with the annotations.

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, mechanism, output contract, refusal contract, usage guidance, and cost trade-off. It is front-loaded with the primary purpose and does not waste words on obvious details already present in the schema or annotations.

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?

Although there is no output schema, the description specifies the full success and failure return contract, including evidence and refusal reasons. It also covers when to use the tool, when not to, and the cost implication. An agent has everything needed to invoke the tool correctly and interpret its result.

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 schema already documents the one real parameter, question, and maps all aliases to it with 100% coverage, so the baseline is 3. The description adds meaning by clarifying that the question is a high-level natural-language request that will be routed and used to fill other tool arguments automatically, rather than a direct API parameter. This goes beyond the schema without duplicating it.

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 identifies a distinct mode: a hallucination-resistant, grounded answer extractor that routes natural-language questions across many tools and answers only from fetched results. It distinguishes itself from the sibling ask_pipeworx by emphasizing evidence, refusal behavior, and high-stakes use. The verb 'extracts' plus the resource scope makes the purpose concrete.

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?

Usage is explicitly scoped: use whenever an answer will be quoted, cited, or acted on and the agent must not invent facts, with concrete domains given. It also states the alternative and trade-off: prefer ask_pipeworx for casual lookups because this mode costs one extra LLM call. This is model-level routing guidance.

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