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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive. The description adds rich behavioral detail: it returns only grounded evidence, includes refusal_reason variants, never fabricates when data is missing, and costs an extra LLM call. There is no contradiction with 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?

Although longer than average, every sentence carries load-bearing information: the grounded mode, mechanism, success/refusal return shapes, concrete use cases, and cost-based alternative. The key differentiator is front-loaded, and the structure is logical.

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?

With no output schema, the description compensates fully by enumerating the success payload, the refusal payload, all five refusal reasons, usage guidance, and cost consideration. An agent has all necessary context to select and invoke the tool correctly.

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 fully documents the question parameter and its aliases (100% coverage), so the description does not need to restate parameter details. The description adds no extra parameter nuance beyond the schema, so the baseline 3 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 identifies a distinct mode ('hallucination-resistant answer mode'), explains the core mechanism (routes like ask_pipeworx, then extracts the answer only from the tool result), and differentiates this tool from the sibling ask_pipeworx by adding evidence and explicit refusals. It is precise about the resource and 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?

It explicitly states when to use the tool ('Use whenever an answer will be quoted, cited, or acted on') and when not to ('prefer ask_pipeworx for casual lookups'), also naming the alternative tool. The high-stakes condition and cost tradeoff give concrete selection criteria.

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