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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, open-world), and the description adds substantial behavior beyond them: the extraction constraint, the full success return shape, the enumerated refusal_reason values, and the extra LLM-call cost. This is exactly the kind of context annotations cannot convey.

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?

Front-loaded with the core value proposition, then the mechanism, then the return contract, then usage. Dense and information-rich with little waste, though the enumeration of return keys and refusal reasons makes it long; every element still earns its place.

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 fully documents both success and failure return shapes, including refusal semantics, which is what an agent needs to interpret results. Nothing material is missing for a single-question read tool.

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% and the schema itself documents that q/text/input/query/prompt are aliases of question. The description adds nothing about parameters, so baseline 3 applies since the schema does all the work.

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?

States a specific capability ('hallucination-resistant answer mode') and explains the mechanism (routes to a tool, then EXTRACTS using only the tool result). It explicitly contrasts with the sibling ask_pipeworx, so an agent can distinguish them without opening schemas.

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?

Gives an explicit when-to-use trigger ('whenever an answer will be quoted, cited, or acted on... must not invent facts') with concrete examples (financial, legal, medical, public statements), and an explicit when-not via 'prefer ask_pipeworx for casual lookups' plus the cost tradeoff.

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