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

The description discloses the exact success and refusal return shapes, lists all refusal_reason enum values, explains that answers come only from tool results, and notes the extra LLM call cost. This goes well beyond the annotations' read-only and idempotent hints.

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, behavior, return format, refusal reasons, usage criteria, and cost tradeoff. It is front-loaded with the most important differentiator and structured logically.

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?

All essential invocation context is provided: what the tool does, when to use it, what it returns, how it refuses, and how it differs from its sibling. The lack of an output schema is mitigated by the explicit return contract in the description.

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 fully documents the single required parameter and all aliases at 100% coverage. The description adds no new parameter-level meaning, so the schema carries the burden; this matches the baseline for high schema coverage.

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 states this is a hallucination-resistant answer mode for high-stakes reads, using a specific verb and resource. It explicitly contrasts itself with ask_pipeworx, making the distinction between grounded extraction and ordinary lookup obvious.

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?

Provides explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' with concrete examples. It also names the alternative, ask_pipeworx, and says to prefer it for casual lookups.

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