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

Beyond annotations, the description reveals important behavioral traits: refusal behavior with specific refusal_reason values, the guarantee to use only tool results, the return shape on success, and the extra LLM call cost. This meaningfully enriches the annotation-provided safety profile without contradiction.

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 value proposition and each sentence adds useful routing, output, or usage detail. It is somewhat dense, but nothing is wasted; the only minor cost is the detailed internal routing explanation that could be trimmed.

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?

Despite lacking an output schema, the description fully documents the success return object and all refusal reasons. It also covers cost trade-offs and usage contexts, making the tool complete for an agent to invoke and interpret 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% and the schema already documents that all parameters are aliases for a natural-language question. The description does not add new parameter-level semantics, so the baseline of 3 applies.

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 the tool as a 'Hallucination-resistant answer mode for high-stakes reads' and distinguishes it by stating it 'EXTRACTS the answer using ONLY what the tool result contains.' It also names the sibling ask_pipeworx and clarifies the differentiation, so an agent can select it correctly.

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?

Usage guidance is explicit: 'Use whenever an answer will be quoted, cited, or acted on' and lists concrete high-stakes domains. It also advises preferring ask_pipeworx for casual lookups and notes the extra LLM call cost, giving clear when-to-use and when-not-to-use signals.

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