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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses the exact success return shape, the full refusal reason enum, and the grounded-only extraction behavior. It also surfaces the extra LLM call cost, which is not captured in 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 dense but every clause earns its place: mode, routing behavior, return contract, refusal behavior, use cases, and cost trade-off. It is front-loaded with the most decision-relevant phrase, 'Hallucination-resistant answer mode for high-stakes reads.'

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 compensates fully by specifying both success and refusal JSON shapes, the refusal reason set, and the sourcing/evidence model. It also covers when to use it versus the sibling, making it complete for an agent to select and invoke 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%, with the question parameter and all aliases already documented, so the description does not need to repeat parameter details. It adds context about routing and argument filling, but no new syntax-level parameter semantics.

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 the tool as a hallucination-resistant answer mode that extracts answers using only tool-result content. It explicitly distinguishes itself from the sibling ask_pipeworx by adding grounded extraction and explicit 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 provides explicit when-to-use guidance: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' with concrete high-stakes examples. It also says to prefer ask_pipeworx for casual lookups and discloses the extra LLM call cost.

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