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

Annotations already declare readOnly, openWorld, and idempotent hints, and the description goes well beyond them by specifying the success response shape, the verbatim evidence field, the refusal reasons enum, and the conditions under which refusals occur. It also discloses the extra LLM call cost. There is no contradiction with the 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 sentence earns its place: mode, routing behavior, return structure, refusal semantics, use cases, and cost tradeoff. It is front-loaded with the most important characterization and contains no filler.

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?

The description is complete for a grounded-answer tool. Since there is no output schema, it fully describes the return payload and failure modes. It also provides usage context, sibling differentiation, and cost guidance, leaving little for an agent to infer.

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 already documents the question parameter and its aliases. The description does not add parameter-level detail beyond saying the tool 'fills arguments' internally, but because the schema handles parameter semantics completely, the baseline score of 3 is appropriate.

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 states a specific mode: a hallucination-resistant, grounded answer mode for high-stakes reads. It explicitly contrasts itself with ask_pipeworx by explaining that it extracts answers only from the tool result. This makes it clearly distinguishable from sibling tools like ask_pipeworx, ask_pipeworx_beta, and validate_claim.

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: whenever an answer will be quoted, cited, or acted on and the agent must not invent facts. It also gives an explicit when-not-to-use: prefer ask_pipeworx for casual lookups, and it states the cost tradeoff of one extra LLM call. This is exemplary usage guidance.

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