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

Beyond the readOnly/idempotent annotations, the description discloses the exact refusal contract with all refusal_reason values, the success payload shape, and the extra LLM-call cost. These behavioral details are not inferable from annotations or schema and directly affect invocation expectations.

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 purpose and then packs only decision-relevant details: routing, extraction rule, result/refusal shapes, usage guidance, and trade-off. No filler; each sentence carries information needed to select and invoke the tool correctly.

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, the description fully covers success and refusal return shapes, including refusal_reason enum values. Combined with the schema's parameter documentation and annotations, an agent has everything required to call this tool correctly and interpret its results.

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%, with each alias explained, so the baseline applies. The description does not add parameter-specific semantics, but it doesn't need to because the input schema fully documents the single required natural-language question and its aliases.

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 names a specific mode ('hallucination-resistant answer mode') with a precise mechanism: route like ask_pipeworx, fetch data, and extract the answer only from the tool result. It also differentiates itself from the sibling ask_pipeworx by the grounding constraint and refusal behavior.

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 states explicit use cases ('quoted, cited, or acted on' high-stakes reads), gives the alternative ('prefer ask_pipeworx for casual lookups'), and quantifies the trade-off (one extra LLM call). This gives an agent clear selection criteria relative to its siblings.

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