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

The description goes well beyond the provided annotations by disclosing exact refusal reasons, the success return shape with evidence and confidence, and the behavior of answering only from tool results. It also reveals the extra LLM call cost, which is not indicated by annotations. There is no contradiction with the readOnly, idempotent, and openWorld 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 dense but well organized, front-loading the most important behavioral distinction, then covering return format, refusal reasons, usage guidance, and cost trade-off. Every sentence contributes meaningful information with no filler.

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?

The description fully equips an agent to select and invoke the tool correctly: it explains routing, answer extraction constraints, success and refusal shapes, refusal reason enums, when to use it, and when to prefer the sibling. Despite lacking an output schema, the return contract is explicitly described.

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 is fully documented at 100% coverage, and the only parameter is the natural-language question with multiple aliases. The description does not add parameter-level detail, but since the schema already carries the meaning, the baseline score 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 names the tool as a hallucination-resistant answer mode for high-stakes reads, with a specific verb and resource. It distinguishes itself from ask_pipeworx by emphasizing that the answer is extracted ONLY from tool results and that refusals are explicit when data doesn't directly answer.

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 explicitly states when to use this tool: whenever an answer will be quoted, cited, or acted on and the agent must not invent facts. It also names the alternative, ask_pipeworx, and instructs to prefer that for casual lookups because this mode costs 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.