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

Annotations already mark readOnly, openWorld, and idempotent hints, and the description adds meaningful behavioral context: it extracts answers only from fetched tool results, returns an explicit refusal when data does not directly answer, lists refusal reasons, and notes an extra LLM call cost. 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 information-dense and front-loaded with the core purpose. It efficiently covers mechanism, return shape, refusal behavior, usage guidance, and cost tradeoff, with every sentence contributing to correct selection and invocation.

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?

Since there is no output schema, the description fully compensates by specifying the success response fields and the refusal reason enum. Combined with the input schema and annotations, an agent has everything needed to choose and call 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 all six parameters are documented aliases for the natural-language question. The description adds no new parameter-level meaning beyond the schema, so the baseline 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 names a specific verb and resource: a grounded, hallucination-resistant answer mode for high-stakes reads. It distinguishes from ask_pipeworx by emphasizing extraction only from tool results and by describing the return shape and 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?

Explicitly says when to use this tool: whenever an answer will be quoted, cited, or acted on and the agent must not invent facts, with concrete domains. It also says to prefer ask_pipeworx for casual lookups and discloses the extra LLM call cost, making the tradeoff clear.

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