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

Annotations already provide readOnly, openWorld, idempotent, and non-destructive hints. The description adds substantial behavioral detail beyond annotations: refusal reason enum, the guarantee to use only tool-result content, the extra LLM call cost, and explicit refusal behavior when data doesn't answer. No contradiction with 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 shape, refusal reasons, use cases, and cost trade-off. It avoids fluff and packs decision-relevant information into a compact block.

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?

Given there is no output schema, the description fully compensates by specifying the success return shape and the refusal contract. The input schema handles the parameter side, annotations handle safety, and the description handles behavioral and selection ambiguity. Nothing important is missing for an agent to invoke this 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%, with the question parameter and its aliases fully described in the schema. The description adds context about internal tool routing and argument filling, but this is more operational behavior than parameter-level semantics. Baseline 3 is appropriate because the schema already carries the parameter documentation burden.

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 function: a hallucination-resistant grounded answer mode for high-stakes reads that extracts answers only from tool results. It explicitly distinguishes itself from ask_pipeworx by emphasizing the grounded extraction behavior and refusal contract, making sibling differentiation clear.

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 explicitly states when to use the tool: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts', with concrete examples. It also gives an exclusion by saying 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.