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

Annotations already declare read-only, open-world, idempotent, non-destructive behavior, so the description is not penalized for omitting those. It adds valuable behavioral specifics: success and refusal response shapes, refusal reason enums, verbatim evidence requirement, and the extra LLM call cost. This goes well beyond the structured fields.

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: core behavior, routing mechanism, response contract, refusal modes, use cases, and cost comparison. Key differentiators are front-loaded and there is no 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?

Since there is no output schema, the description fully carries the burden of explaining return values and does so with concrete JSON shapes and refusal reasons. Combined with annotations covering side-effect safety and the explicit usage context, 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%, with each parameter alias documented in the input schema itself. The tool description does not add parameter-level semantics beyond noting that routing automatically fills arguments, so baseline 3 is appropriate.

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 names a specific behavior: a hallucination-resistant answer mode for high-stakes reads that extracts answers only from tool results. It explicitly contrasts itself with ask_pipeworx by emphasizing grounded extraction and refusal behavior, making sibling differentiation clear.

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 states when to use this tool ('whenever an answer will be quoted, cited, or acted on'), provides concrete domains (financial verdicts, legal claims, medical lookups, public statements), and tells the agent to prefer ask_pipeworx for casual lookups due to the extra LLM call. This is strong when-to-use and when-not-to-use guidance.

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