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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses a detailed behavioral contract: it returns evidence as verbatim quotes, includes confidence and source, returns structured refusal reasons, and costs an extra LLM call. There is 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 sentence contributes: core behavior, routing mechanics, return contract, usage guidance, and cost tradeoff. It could be slightly tightened, but it is well front-loaded and not bloated.

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 present, the description compensates by specifying the full success return shape and the complete refusal_reason enum. It also covers cost, usage context, and failure behavior, so an agent has everything needed to invoke it 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 all six parameters documented as aliases for the natural-language question. The description adds no new parameter-level meaning, but the schema already carries the full burden, so the baseline score of 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 states a precise purpose: a hallucination-resistant answer mode that extracts answers only from fetched tool results. It explicitly differentiates from the sibling ask_pipeworx by grounding and refusal behavior, making the tool's identity and role unmistakable.

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 gives explicit when-to-use guidance ('whenever an answer will be quoted, cited, or acted on... must not invent facts'), with concrete high-stakes examples, and explicitly points to ask_pipeworx as the alternative for casual lookups. This leaves no ambiguity about tool selection.

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