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

The description goes well beyond the annotations by disclosing the exact success and refusal response shapes, the strict evidence-based extraction approach, and the refusal reasons. It also discloses the extra LLM call cost, which the annotations do not express. There is no contradiction with the annotations; all hints align with a read-only, idempotent, non-destructive tool.

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 dense but every clause contributes: purpose, routing behavior, output contract, refusal behavior, usage guidance, and cost trade-off. It is front-loaded with the core decision-relevant information and contains no filler.

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 having no output schema, the description fully documents the return contract and refusal reasons, which is critical for an agent deciding whether the answer is usable. It also covers when to use the tool, when not to, and how it compares to ask_pipeworx, leaving no major decision-relevant gap.

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%, so the baseline applies: the parameter semantics are fully documented in the schema. The description adds context about how the question is routed and grounded, but it does not add new meaning beyond the schema's aliases and natural-language question explanation.

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 opens with a specific, identifiable purpose: a hallucination-resistant answer mode for high-stakes reads. It clearly names the sibling tool (ask_pipeworx) and positions itself as the grounded, evidence-backed variant, so an agent can distinguish it immediately.

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?

Use guidance is explicit and contextual: use when answers will be quoted, cited, or acted on, and when the agent must not invent facts. It names concrete high-stakes domains (financial verdicts, legal claims, medical lookups, public statements) and tells the agent to prefer ask_pipeworx for casual lookups, which also addresses the alternative.

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