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

The annotations already convey read-only, idempotent, non-destructive behavior. The description adds significant behavioral transparency beyond that: it details the exact success return shape, the explicit refusal mechanism with specific refusal_reason values, and the guarantee that answers are extracted only from tool results. This is especially valuable because there is no output schema.

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?

Though the description is a substantial paragraph, every sentence earns its place by covering purpose, behavior, return contract, refusal reasons, usage guidance, and cost tradeoff. The most critical differentiator ('hallucination-resistant', 'only what the tool result contains') is front-loaded, and no filler is present.

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?

Given there is no output schema ticket, the description compensates by embedding the return format and refusal reasons directly in prose. It also covers cost, routing, use cases, and when not to use itched. For an agent deciding whether and how to invoke this tool, the description is self-sufficient and complete.

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?

The input schema fully documents all parameters, all being aliases of 'question' with 100% description coverage. The description reinforces that the input is a natural language question and that the tool fills arguments internally, but it does not add new semantic details about parameter usage or formatting beyond the schema. 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 clearly identifies a specific mode ('Hallucination-resistant answer mode for high-stakes reads') and explains the core behavior: it routes through the same pipeline as ask_pipeworx, fetches data, then extracts an answer strictly from the tool result. It also differentiates itself from sibling ask_pipeworx by emphasizing non-invention of facts and explicit refusal behavior.

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?

Explicitly states when to use this tool ('whenever an answer will be quoted, cited, or acted on') and gives concrete example contexts such as financial verdicts, legal claims, medical lookups, and public statements. It also provides an exclusion: prefer ask_pipeworx for casual lookups, and notes the cost tradeoff of an extra LLM call.

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