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

Description goes beyond annotations by detailing that it extracts only from tool results, returns explicit refusal reasons when data is insufficient, and notes the extra LLM call cost. This fully discloses behavior and side effects.

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?

Description is informative but slightly verbose; the dash-separated structure aids readability and the key points are front-loaded. Minor redundancy in listing refusal reasons and aliases but overall well-organized.

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?

Despite no output schema, the description fully explains the return format, refusal cases, use cases, and cost trade-off. This is sufficient for an agent to decide 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?

Schema covers 100% of parameters with aliases and descriptions, so the baseline applies. The tool description itself adds no extra parameter meaning beyond the schema.

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?

Description clearly states it is a hallucination-resistant answer mode for high-stakes reads, and explicitly contrasts with ask_pipeworx by noting the same routing but grounded extraction. This makes the tool's purpose distinct and unmistakable.

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?

Description explicitly says to use when an answer will be quoted, cited, or acted on, and to prefer ask_pipeworx for casual lookups. This gives clear when-to-use and when-not-to-use guidance relative to the sibling tool.

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