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

Beyond the annotations (read-only, idempotent, non-destructive), the description discloses the refusal behavior, the exact return structure, the evidence extraction mechanism, and the additional LLM call cost. This is rich behavioral context that helps the agent predict outcomes.

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 information-dense but not overly long. It front-loads the core purpose and then covers return format, refusal reasons, usage, and cost trade-off. Every sentence contributes, though a more structured layout could improve scannability.

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 complex tool with no output schema, the description is remarkably complete. It covers success and failure return structures, refusal reasons, use cases, and cost implications. The routing mechanism is not detailed but not needed for calling 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?

Schema description coverage is 100%, and the description does not add significant parameter semantics beyond what the schema already provides. The description mentions natural language questions but no additional format or constraint guidance, so 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 states the tool's function: a hallucination-resistant answer mode for high-stakes reads. It explicitly differentiates from the sibling ask_pipeworx by the grounded extraction and refusal mechanism, making the purpose and distinction unambiguous.

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 provides explicit usage guidance: use when answers will be quoted/cited/acted on and facts must not be invented, and prefer ask_pipeworx for casual lookups. It also notes the extra LLM call cost, giving a clear decision rule.

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