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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), and the description still adds substantial behavior beyond them: the full success return shape, the enumerated refusal_reason enum, the strict grounding constraint (answers derived ONLY from tool result content), and the extra-LLM-call cost. This is rich behavioral disclosure no structured field could express.

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 core purpose is front-loaded in the first clause, and each of the five sentences earns its place: mechanism, return shape, refusal enum, usage domains, and cost trade-off. It is dense rather than padded, though slightly long — the length is justified by the absence of an output schema.

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?

With no output schema present, the description fully documents both the success and refusal return shapes, enumerates all refusal reasons, states the cost/accuracy trade-off, and gives decision-relevant usage examples. The only implicit dependency is knowledge of ask_pipeworx's routing, which is named and available in the sibling list.

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% — the question parameter and all five aliases are already fully documented in the schema. The description adds only the contextual hint that the question is natural language and will be routed, which is marginal; baseline 3 applies since the schema does the heavy lifting.

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?

States a precise behavior — hallucination-resistant answer extraction grounded strictly in tool results — and distinguishes itself from sibling ask_pipeworx via the 'same routing... then EXTRACTS' contrast. The refusal mechanism and evidence-quote return shape make the tool's identity unmistakable.

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?

Explicitly names when to use ('whenever an answer will be quoted, cited, or acted on') with concrete high-stakes domains (financial, legal, medical, public statements), and when not to ('prefer ask_pipeworx for casual lookups'), naming the alternative directly. Nothing is left to inference.

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