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

The description discloses key behavioral traits beyond annotations: it extracts only from tool results, returns an explicit refusal with enumerated reasons, and costs an extra LLM call. No contradiction with readOnly/openWorld/idempotent 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 not bloated; it packs purpose, usage, output shape, refusal reasons, and cost into a compact paragraph. Minor redundancy between the routing explanation and the cost comparison, but overall efficient.

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?

Despite lacking an output schema, the description thoroughly explains return values, refusal reasons, and success/failure shapes. It also provides context on cost and alternatives, making it complete for an agent deciding whether and how to invoke it.

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 single natural-language question. The question description adds useful context by explicitly listing accepted aliases, slightly exceeding baseline.

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 identifies the tool as a hallucination-resistant answer mode that routes through the same pipeline as ask_pipeworx and extracts answers only from tool results. It differentiates itself from sibling ask_pipeworx by emphasizing grounded output 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 states when to use the tool ('whenever an answer will be quoted, cited, or acted on') and when to prefer the cheaper alternative ('prefer ask_pipeworx for casual lookups'). This gives clear selection criteria among siblings.

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