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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses the extraction-only behavior, the exact success and refusal return shapes, possible refusal reasons, evidence quoting, and the extra LLM call cost. This substantially exceeds what annotations provide and does not contradict them.

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?

Every sentence carries decision-relevant information: behavior, return contract, refusal cases, use case, and cost comparison. It is front-loaded with the defining trait and wastes no words.

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, so the description correctly carries the full return contract including success fields and refusal reasons. Combined with the input schema and annotations, an agent has everything needed to select and invoke this 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 coverage is 100%, and the schema itself describes the question parameter and its aliases. The description does not add parameter-level detail, but with full schema coverage it does not need to; baseline 3 applies.

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 opens with a specific, differentiating verb and resource: 'Hallucination-resistant answer mode for high-stakes reads,' and explains it uses the same routing as ask_pipeworx but extracts answers only from tool results. This makes it immediately distinguishable from the sibling ask_pipeworx and other research tools.

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 exactly when to use this mode ('whenever an answer will be quoted, cited, or acted on... must not invent facts') and explicitly names the alternative for lighter cases ('prefer ask_pipeworx for casual lookups') plus the cost tradeoff. This is explicit when/when-not guidance.

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