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

The description goes far beyond the annotations by specifying the exact success payload, the explicit refusal reasons, the grounded-extraction behavior, and the extra LLM call cost. Annotations already declare read-only and idempotent, and the description adds substantial behavioral detail without contradiction.

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?

Four dense sentences, with the core purpose front-loaded and every sentence adding value: behavior, return shape, usage conditions, and tradeoff vs. the sibling tool. There is no filler or redundant restatement.

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 carries the full burden of explaining the return contract, and it does so thoroughly. It also covers success and refusal paths, use cases, and cost tradeoffs, making the tool self-sufficient for correct invocation.

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%, so the description does not need to re-document parameters. It adds contextual framing about high-stakes use but no parameter-specific detail beyond what the schema already provides, which matches the baseline for full schema coverage.

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 immediately identifies this as a 'Hallucination-resistant answer mode for high-stakes reads' and explicitly contrasts it with ask_pipeworx by noting 'same routing' but grounded extraction. This clearly distinguishes it from the sibling tool and states the resource and purpose.

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 tool gives explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on' and names the alternative: 'prefer ask_pipeworx for casual lookups.' This is a clear, actionable decision rule.

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