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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive behavior. The description adds meaningful behavioral context beyond these: it only answers from tool results, returns a structured refusal with specific reasons, includes verbatim evidence, and costs an extra LLM call. This fully discloses the tool's safety and refusal behavior without contradicting 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, routing behavior, return shape, refusal reasons, usage guidance, and cost. The most important distinction—grounded vs. casual—is front-loaded, and the structure is internally organized from behavior to usage.

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?

Because there is no output schema, the description properly explains the return format, including success and refusal variants with refusal_reason enum values. It also covers when to use the tool, the alternative, and the extra cost. Nothing critical is missing for an agent to select and invoke this tool 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 description coverage is 100%, so the baseline is 3. The description does not add much parameter-level detail, but the schema already documents that question accepts aliases such as q, prompt, text, and input, which is sufficient for correct invocation.

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 this as a hallucination-resistant answer mode for high-stakes reads, distinguishes it from the sibling ask_pipeworx, and states what it does: extract answers only from tool results, with evidence and explicit refusals. It names a specific behavior and return shape, making the tool's purpose immediately recognizable.

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 explicitly says when to use this tool: whenever an answer will be quoted, cited, or acted on and the agent must not invent facts, with concrete examples like financial verdicts, legal claims, and medical lookups. It also explicitly directs users to prefer ask_pipeworx for casual lookups and discloses the extra LLM call cost, providing clear routing guidance.

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