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

Beyond the readOnlyHint and idempotentHint annotations, the description discloses important behavioral traits: the tool returns evidence as a verbatim quote, refuses explicitly when the data does not answer, enumerates the refusal reasons, and costs one extra LLM call. It also emphasizes strict grounding, using only what the tool result contains. No contradiction with annotations exists.

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 sentence earns its place: purpose, mechanism, return contract, refusal behavior, usage guidance, and cost trade-off. It is front-loaded with the defining trait and does not repeat schema information unnecessarily.

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?

There is no output schema, so the description correctly provides the full success and refusal return shapes, including refusal_reason values. It also covers when to use the tool, the extra cost, and the relation to ask_pipeworx, making the tool fully actionable without requiring external knowledge.

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?

The input schema already fully documents the 'question' parameter and its aliases with 100% coverage, so the description does not need to add much. The description implies high-stakes factual questions but does not add new parameter-level semantics beyond what the schema provides, hence the baseline score of 3.

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 identifies the tool as a hallucination-resistant, grounded answer mode for high-stakes reads, and explains that it extracts answers ONLY from the fetched tool result. It also distinguishes itself from the sibling ask_pipeworx while naming it directly, so an agent can tell them apart.

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 explicitly states when to use the tool: whenever an answer will be quoted, cited, or acted on, and facts must not be invented, with concrete domains like financial verdicts, legal claims, medical lookups, and public statements. It also states when not to use it: prefer ask_pipeworx for casual lookups, giving a clear exclusion and the reasoning (extra LLM call).

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