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

Beyond the readOnly/idempotent annotations, the description reveals the crucial behavioral trait: the tool refuses rather than fabricates, enumerating refusal_reason values. It also discloses an extra LLM call cost and the strict evidence/verbatim-quote requirement, which annotations could not 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?

The description is longer than average, but every part earns its place: purpose, behavioral contract, use cases, and cost trade-off. The enumerated refusal-reason list is verbose but materially changes how an agent interprets a null answer, so it is justified.

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 compensates by documenting the success shape, refusal shape, refusal reasons, use timing, and cost comparison. For a one-required-parameter tool, this is complete enough for an agent to select and invoke it 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%: all six parameters are documented aliases for the same natural-language question. The description does not need to add parameter semantics, so baseline 3 is appropriate; it neither detracts nor adds 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?

The description names a specific mode ('hallucination-resistant answer mode'), the mechanism ('extracts the answer using ONLY what the tool result contains'), and the return contract including refusals. It explicitly contrasts itself with ask_pipeworx, making the distinction from siblings clear.

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?

It states exactly when to use it: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' with concrete domains. It also tells the agent when not to use it: 'prefer ask_pipeworx for casual lookups,' backed by a concrete cost difference.

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