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

Beyond the readOnly/idempotent annotations, the description discloses the refusal-driven behavior with specific refusal_reason values, the success response shape including verbatim evidence and confidence, and the cost tradeoff of one extra LLM call. There is no contradiction with 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?

Dense but efficient: purpose, mechanism, return/refusal contract, usage guidance, and cost tradeoff are each covered in one sentence. The most important differentiators (grounded, evidence-based, refusal behavior) are front-loaded, and no sentence is 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, the description supplies the return fields and refusal reasons needed to interpret results. It also covers cost, alternatives, and the exact conditions under which the tool should be selected, making it complete for an agent deciding whether and how to invoke it.

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 description coverage is 100%, so the schema already fully documents all six params as aliases for the question string. The description adds no parameter-specific meaning beyond confirming natural-language questions, which the schema already states; baseline 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?

Opening sentence states a specific mode: a hallucination-resistant answer mode for high-stakes reads, using a grounded evidence-based extraction. It explicitly contrasts with ask_pipeworx by sharing routing but constraining the answer to information in the tool result, which separates it from sibling ask_pipeworx and ask_pipeworx_beta.

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?

Gives explicit conditions for use: whenever an answer will be quoted, cited, or acted on, and when the agent must not invent facts, with concrete high-stakes examples. It also states the alternative and when to prefer it: use ask_pipeworx for casual lookups because this mode costs 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.