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

Beyond the annotations (read-only, idempotent, non-destructive), the description reveals important behavior: it refuses rather than fabricates when data doesn't answer, provides specific refusal reasons, returns verbatim evidence, and costs an extra LLM call. This is substantial behavioral context beyond what annotations convey.

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 earns its place: it front-loads the core value proposition, then details return/refusal contracts, then gives clear usage guidance and cost trade-off. No filler or repetition.

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?

For a one-parameter tool with no output schema, the description thoroughly covers invocation context, return behavior, refusal modes, and alternatives. An agent has everything needed to decide when to call it and what to expect.

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 description coverage is 100% for the single required question parameter, including alias documentation. The description itself adds no parameter-specific meaning, but it doesn't need to because the schema already fully documents the input.

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 a specific mode: hallucination-resistant grounded answering that extracts answers only from tool results, with explicit return/refusal shapes. It also differentiates from the ask_pipeworx sibling by noting the same routing but an extra extraction step and extra cost.

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 ('whenever an answer will be quoted, cited, or acted on', high-stakes domains) and when not to ('prefer ask_pipeworx for casual lookups'). It also names the alternative directly and gives the trade-off (one extra LLM call).

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