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

The annotations already declare the operation is read-only, open-world, idempotent, and non-destructive. The description adds substantial behavioral detail beyond that: exact success return shape, explicit refusal reasons, reliance on verbatim evidence, and the extra LLM call cost. This fully discloses how the tool behaves when data is insufficient.

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 section earns its place: mode definition, routing behavior, return contract, refusal conditions, use cases, and cost trade-off. It is front-loaded with the most important decision signal ('hallucination-resistant ... high-stakes reads') and remains structured rather than rambling.

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 specifies success and refusal return shapes, including refusal reasons. It also explains when this tool is preferable, what distinguishes it from the sibling, and the cost trade-off. An agent has enough context to invoke it correctly and interpret its result.

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%, meaning the question parameter and its aliases are fully documented in the input schema. The description does not add parameter-level details beyond that, which is acceptable but not additive. Baseline 3 is appropriate because the schema carries the parameter semantics.

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'), a clear resource (ask_pipeworx_grounded), and a distinct behavior: extracting answers only from tool results. It explicitly contrasts with the sibling ask_pipeworx, so an agent can tell them apart 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?

It gives explicit when-to-use guidance: whenever an answer will be quoted, cited, or acted on and the agent must not invent facts. It also gives a when-not-to-use directive: prefer ask_pipeworx for casual lookups, because this mode costs an extra LLM call.

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