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

Annotations already mark it readOnly, openWorld, idempotent, and non-destructive, but the description adds valuable behavior beyond that: it extracts answers using ONLY the tool result, returns a structured refusal instead of inventing facts, and lists all refusal reasons. It also discloses the cost of one extra LLM call, which is behavioral context not present in annotations.

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 efficient; every sentence serves a distinct purpose: defining the mode, describing the mechanism, detailing return/refusal shapes, giving use cases, and noting cost trade-offs. It is longer than strictly necessary but justified by the tool's complexity and the need to distinguish it from ask_pipeworx.

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?

The description covers the tool's purpose, behavior, return/refusal contract, use cases, and cost trade-offs. Even without an output schema, the agent has enough information to decide when to call it and what to expect back. Nothing critical is missing.

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%, so the input schema already documents the question parameter and its aliases. The description does not add any parameter-level semantics, but with full schema coverage it does not need to. 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 a specific mode: a grounded, hallucination-resistant answer extraction for high-stakes reads. It explicitly contrasts with ask_pipeworx by stating 'Same routing as ask_pipeworx' but adding the grounded extraction behavior, making it distinguishable from siblings.

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?

Provides explicit when-to-use guidance: use whenever answers will be quoted, cited, or acted on, with concrete examples (financial verdicts, legal claims, medical lookups, public statements). It also gives an exclusion: prefer ask_pipeworx for casual lookups because of the extra LLM call cost.

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