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

The annotations already indicate read-only, open-world, and idempotent behavior. The description adds transparent details about the extra cost and the refusal-based output modes without contradicting 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 informative but somewhat redundant, repeating the return format and the refusal_reason list twice. It could be trimmed without losing meaning.

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 fully covers the tool's behavior, differences from the sibling tool, cost trade-off, and output structure, leaving no important context missing for selection and invocation.

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 schema has 100% coverage for all six parameters, each documented as an alias for 'question'. The description adds no additional parameter semantics beyond what the schema already states, so a baseline score 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 clearly states the tool's purpose as a hallucination-resistant answer mode for high-stakes reads, explicitly distinguishing it from the sibling ask_pipeworx by describing the extraction process and the return format.

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 usage guidance: use when the answer will be quoted, cited, or acted on, and prefer ask_pipeworx for casual lookups due to 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.