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

Annotations carry readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, but the description adds substantial behavioral detail: the exact success/refusal JSON shapes, the complete list of refusal_reason enums, and the cost implication of one extra LLM call. It also clarifies that it uses only the tool result content, which is a safety-relevant 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: it states the core distinction, gives the return/refusal contract, supplies usage conditions, and provides a cost tradeoff. It is front-loaded with the most important qualifier ('Hallucination-resistant answer mode') and avoids redundant 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?

There is no output schema, so the description must and does explain the return values, including both success and refusal shapes and the meaning of refusal reasons. It also covers when to use, when not to use, and the cost delta. Nothing essential for calling this tool correctly is missing.

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 the 'question' parameter and its aliases. The description does not add parameter-specific detail beyond implying that the question drives tool routing and argument filling, which is expected. Baseline 3 applies because the schema does the heavy lifting.

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 verb+resource: this is a 'grounded' answer mode that extracts answers only from tool results, with explicit refusal behavior. It differentiates itself from the sibling ask_pipeworx by emphasizing the extraction constraint and the refusal reasons.

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 provided: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' with concrete examples (financial verdicts, legal claims, medical lookups). It also names the alternative (ask_pipeworx) and states when to prefer it ('for casual lookups'), plus notes 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.