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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive behavior. The description adds substantial context beyond that: it discloses the exact refusal reasons, the grounded extraction constraint, the extra LLM call cost, and what happens when data is missing or confusing. No contradiction with 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 information-dense but each sentence serves a purpose: defining the mode, contrasting with ask_pipeworx, detailing return and refusal shapes, and stating when to use it. It is front-loaded with the most important distinction and remains scannable.

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?

There is no output schema, yet the description fully explains the return structure and refusal semantics. Combined with complete parameter schema coverage and explicit sibling differentiation, an agent has everything necessary to invoke the tool correctly and interpret its response.

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 fully documents all six parameters, all of which are aliases for the same natural-language question. The description adds no additional parameter-level meaning, but with 100% schema coverage the baseline of 3 is appropriate.

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 a grounded, hallucination-resistant answer mode for high-stakes reads, with explicit success and refusal return shapes. It also distinguishes itself from ask_pipeworx by emphasizing extraction only from tool results and the refusal behavior 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?

Provides explicit guidance: use when answers will be quoted, cited, or acted on and facts must not be invented, and prefer ask_pipeworx for casual lookups due to the extra LLM call cost. This makes the selection decision concrete and actionable.

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