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

It clearly discloses that the tool extracts answers only from tool result content, returns evidence verbatim, and explicitly refuses with defined refusal_reason values when data doesn't directly answer. It also reveals the extra LLM call cost. These behavioral details go well beyond the annotations' read-only and 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 dense but every sentence adds value: purpose, routing, return shape, refusal reasons, use cases, and cost tradeoff. It is front-loaded with the core purpose and avoids 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?

The description covers behavior, return format, refusal semantics, cost, alternative selection, and appropriate contexts. There is no output schema, so the description's explicit return and refusal shape fills that gap completely. The input schema handles parameter 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 provides 100% parameter documentation, including the natural-language question parameter and its aliases. The description does not add parameter-specific detail, but the full schema coverage makes this acceptable 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 clearly identifies a specific mode: a hallucination-resistant, grounded answer extraction tool that returns answers only from fetched tool results. It distinguishes itself from the sibling ask_pipeworx by emphasizing extraction from evidence and refusal behavior rather than casual lookup.

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?

The description gives explicit when-to-use guidance: when answers may be quoted, cited, or acted on and the agent must not invent facts, with examples like financial verdicts, legal claims, and medical lookups. It also gives an explicit alternative: prefer ask_pipeworx for casual lookups due to the extra LLM call cost.

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