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

The description discloses substantial behavior beyond the annotations: it returns a structured object with evidence (verbatim quote), confidence, source, and fetched_at; it may refuse to answer and enumerates the five refusal reasons; and it explicitly states the guarantee that the answer is extracted only from the tool result. The annotations (readOnly, openWorld, idempotent, non-destructive) are consistent and the description adds important operational context like the extra LLM call cost.

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 front-loaded with the core purpose ('Hallucination-resistant answer mode') and then delivers all essential details in a compact, well-organized flow: routing behavior, return structure, refusal cases, use cases, and cost trade-off. Every sentence earns its place, and there is no filler or redundancy.

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 fully compensates by spelling out the exact success response shape and the refusal response with all possible refusal_reason values. It also covers when to use the tool and its cost implication. For a read-only question-answering tool with this complexity, nothing an agent needs to call it 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?

The schema already provides 100% description coverage, with the 'question' parameter and all aliases fully documented. The description does not add new parameter semantics beyond implying the input is a natural-language question, which the schema already states. Per the baseline rule, a 3 is appropriate when the schema carries the full weight.

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 a specific, evocative purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It clearly distinguishes itself from the main sibling by stating it 'extracts the answer using ONLY what the tool result contains,' which contrasts with the regular ask_pipeworx. The differentiation extends to cost and use case, making it easy for an agent to know exactly what this tool does and how it differs.

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 usage conditions are given: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements).' It also names the alternative and the trade-off: 'Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.' This leaves no ambiguity about when to choose this tool over its sibling.

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