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

Annotations already cover read-only, open-world, idempotent, and non-destructive behavior, so the bar for added disclosure is met here by detailing refusal modes, return shape, data-grounding behavior, and the extra LLM call cost. These are important operational behaviors an agent could not infer from annotations alone.

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 typical descriptions, every sentence carries necessary operational information: behavior, return contract, refusal reasons, usage context, and cost trade-off. The key differentiator is front-loaded, and the structure flows logically from mode to behavior to usage.

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?

There is no output schema, so the description compensates by fully specifying the success and refusal response shapes. It also covers routing behavior, cost, and when to prefer this tool over ask_pipeworx, making it complete for an agent to select and invoke 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 schema fully documents the question parameter and all its aliases with 100% coverage. The description adds no additional parameter semantics beyond what the schema already provides, so the baseline score of 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 starts with a specific, meaningful characterization: 'Hallucination-resistant answer mode for high-stakes reads.' It then distinguishes this tool from ask_pipeworx by grounding the answer strictly in tool results and by returning explicit refusals when the data cannot answer. An agent can immediately tell what this tool does and how it differs from its siblings.

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 when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' with concrete examples. It also names the alternative for casual use: 'prefer ask_pipeworx for casual lookups.' This is clear selection guidance with no ambiguity.

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