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

Goes well beyond the annotations by disclosing the extra LLM call cost, the strict evidence-only extraction rule, the exact success return shape, and the refusal contract with specific refusal reasons. The description adds rich behavioral context and does not contradict any annotation.

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 efficient and front-loads purpose, then moves through return contract, usage criteria, and cost trade-off. Every sentence serves a distinct decision-relevant purpose, and there is no filler or repetition.

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?

Because there is no optional but well-developed output schema, the description compensates by spelling out both success and refusal response shapes. Combined with full schema param coverage and the explicit cost/usage trade-off, the agent has everything needed to select and invoke the tool correctly.

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%, so the parameters are already fully documented and the description does not need to explain them. The description references internal argument filling but adds no parameter-specific meaning beyond what the input schema provides, matching 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 explicitly defines the tool as a hallucination-resistant answer mode for high-stakes reads, with concrete behavior: it routes like ask_pipeworx, fetches from a tool result, and extracts answers only from that content. This clearly distinguishes it from the base ask_pipeworx tool and other siblings.

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 when-to-use guidance: whenever an answer will be quoted, cited, or acted on, and in domains where invented facts are unacceptable (financial verdicts, legal claims, medical lookups, public statements). It also names the alternative ask_pipeworx and says to prefer it for casual lookups, giving clear when-not-to-use direction.

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