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

Beyond the readOnly/idempotent annotations, the description discloses important behaviors: it only uses tool result content, will refuse rather than invent facts, returns a structured refusal_reason, and costs one extra LLM call. This is rich, actionable transparency with no contradiction to 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 fairly long but every sentence carries essential operational information: behavior, return shape, refusal cases, usage context, and cost trade-off. It is front-loaded with the key behavioral distinction and structured so an agent can quickly extract decision-relevant facts.

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 compensates by specifying the exact success and refusal response shapes, including refusal_reason values. Combined with the usage and cost guidance, an agent has everything needed 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?

The schema already fully documents the question parameter and its aliases, so the description does not need to add parameter-level detail. The description adds no parameter-specific semantics, but does not need to given 100% schema coverage, meeting the baseline.

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 states a specific behavior: a hallucination-resistant, grounded answer mode that extracts answers only from tool results. It distinguishes itself from ask_pipeworx by explicitly contrasting the grounded extraction and refusal behavior, so an agent can select it correctly.

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 gives explicit when-to-use guidance: use when answers will be quoted, cited, or acted on in high-stakes contexts. It also directly says to prefer ask_pipeworx for casual lookups and notes the extra LLM call cost, giving a clear decision rule.

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