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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description goes beyond this by detailing the exact return structure, the five refusal_reason values, and the cost implication (one extra LLM call). This adds substantial behavioral context 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 dense but well-structured: it leads with the core purpose, explains the routing and extraction mechanism, lists return fields, provides refusal reasons, and gives usage guidance. Every sentence earns its place; it is not verbose given the complexity, though it is on the longer side.

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 the tool's complexity (routing across 5,801 tools, extraction, refusal logic) and the absence of an output schema, the description fully equips an agent to call it correctly and interpret results. It covers purpose, behavior, return format, error handling, and cost — nothing essential 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?

Schema coverage is 100% — all six parameters are documented with descriptions, and they are aliases for the same 'question' field. The description does not add any parameter-level detail beyond what the schema already provides, so the baseline 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 opens with 'Hallucination-resistant answer mode' — a specific, non-tautological verb phrase that clearly states the tool's core function. It distinguishes itself from the sibling ask_pipeworx by emphasizing extraction with evidence and refusal behavior, making its purpose unambiguous.

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?

It explicitly states when to use this tool ('whenever an answer will be quoted, cited, or acted on...') and when not to ('prefer ask_pipeworx for casual lookups'), including a cost tradeoff. This is a textbook example of usage guidance.

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