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

Beyond the readOnly and idempotent annotations, the description discloses the refusal behavior, the exact success and failure return shapes, the verbatim-evidence guarantee, and the extra LLM call cost. This gives the agent a strong model of how the tool behaves and what it will not do.

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 long but information-dense, with the core purpose front-loaded and every clause adding context: routing, extraction, return shape, refusal reasons, use cases, and cost tradeoff. The inline return-shape enumeration is somewhat verbose, but it compensates for the lack of an output schema.

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 a simple one-parameter input schema, no output schema, and safety-related annotations already provided, the description fully covers what the agent needs: when to use it, what it returns, how it refuses, and how it differs from its sibling. Nothing critical 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 schema already fully documents the 'question' parameter and its aliases, so the description does not need to add parameter-level detail. The description adds context about routing and grounding, but no additional meaning about how the parameter should be formatted or used.

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 this as a hallucination-resistant answer mode that routes across tools and extracts answers only from the fetched result. It explicitly contrasts itself with ask_pipeworx, making the tool's distinct role unambiguous.

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 states exactly when to use this tool ('whenever an answer will be quoted, cited, or acted on') and when to prefer the alternative ('prefer ask_pipeworx for casual lookups'). It also notes the extra LLM call cost, giving the agent a concrete decision rule.

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