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

Annotations only indicate safe, read-only, idempotent behavior, so the description carries the full burden of behavioral disclosure. It richly delivers by describing the refusal contract with exact refusal_reason values, the requirement for verbatim evidence, and the additional cost versus the sibling. 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?

The description is longer than minimal but every sentence conveys necessary signal: purpose, mechanism, response shape, refusal reasons, use cases, and cost trade-off. It is front-loaded with the core purpose, though the full return-shape enumeration makes it denser than strictly required.

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 compensates by specifying both success and refusal response structures, including refusal_reason enum values. It also provides cost and sibling comparison, making the tool fully understandable and safely invocable without additional context.

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% and the schema already documents all six aliases, including 'Accepts query, q, prompt, text, input as aliases.' The description adds no parameter-specific meaning, but none is needed because the schema fully carries that load.

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 opens with 'Hallucination-resistant answer mode for high-stakes reads,' specifying a distinct mode and use case. It explicitly differentiates from ask_pipeworx by noting the same routing but extraction-only, grounding behavior, making it clear what this variant does and why it exists.

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: 'Use whenever an answer will be quoted, cited, or acted on' and lists concrete high-stakes domains. It also provides the alternative condition: 'prefer ask_pipeworx for casual lookups' and quantifies the trade-off as 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.