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

Beyond the annotations (readOnly, openWorld, idempotent), the description discloses the additional LLM call cost and the explicit refusal mechanism with specific refusal_reason values. This is important behavioral information not present in 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 slightly verbose but well-structured, leading with the core purpose, then the mechanism, return format, and usage guidance. The repetition of 'ask_pipeworx' is functional for comparison, though it adds some length.

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 the simple parameter set and no output schema, the description provides a complete picture: behavior, return shape, refusal reasons, cost, and comparison to the alternative. Nothing essential is missing.

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?

The input schema already fully documents all six parameters as aliases for a single question string (100% coverage). The description adds no further parameter-specific detail, so the score stays at the baseline of 3.

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 clearly states the tool is a hallucination-resistant answer mode that extracts answers only from tool results, with explicit mention of evidence and refusal behavior. It differentiates itself from the sibling ask_pipeworx tool by its grounding guarantee.

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 this tool—when the answer will be quoted, cited, or acted on and facts must not be invented—and when to prefer ask_pipeworx for casual lookups, due to the extra LLM call cost. This provides clear selection criteria.

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