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

The description adds substantial behavioral detail beyond the readOnly/idempotent annotations: it explains the refusal mechanism with specific refusal_reason values, the guarantee to use only tool result contents, and the extra LLM call cost. This meaningfully informs an agent about failure modes and performance trade-offs.

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 front-loads the core purpose and then provides structured value: behavioral guarantee, return/refusal shape, and usage guidance. A little redundancy exists between 'hallucination-resistant' and 'must not invent facts,' but overall every sentence contributes useful selection or invocation information.

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 read-only, idempotent tool with rich annotations, the description is complete enough: it explains the return contract, refusal reasons, cost trade-off, and usage context. There is no output schema, so the explicit description of {answer, evidence, confidence, source, fetched_at, refusal_reason} fills that 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 input schema already documents the question parameter and its aliases. The description doesn't add parameter-level detail, but the schema suffices; the tool's only real parameter is well-covered, 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 identifies this as a grounded, hallucination-resistant answer mode for high-stakes reads, distinguishing it from the sibling ask_pipeworx by explaining the same routing but stricter extraction from tool results only. It states the verb-like action (answer extraction) and the resource scope precisely.

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?

Explicitly says when to use the tool ('whenever an answer will be quoted, cited, or acted on') and when not to ('prefer ask_pipeworx for casual lookups'), while naming the alternative. It also provides domain examples like financial verdicts and medical lookups, giving an agent concrete routing criteria.

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