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

Annotations already establish read-only, idempotent, open-world behavior. The description goes beyond them by disclosing the refusal mechanism, the exact success and refusal result shapes, the evidence extraction behavior, and the extra LLM call cost. This gives the agent a precise behavioral model without contradicting the 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 tightly organized: what the mode does, how it routes, what it returns, when to use it, and the cost trade-off. Every clause earns its place, and the most decision-relevant phrase ('Hallucination-resistant answer mode for high-stakes reads') is front-loaded.

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?

Despite having no output schema, the description fully specifies the success return envelope and the refusal reasons, which is critical for callers. It also covers routing behavior, grounding constraints, and cost implications. For a tool of this complexity, nothing essential 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 description coverage is 100%, and the schema already explains the question parameter and its aliases. The description adds no new parameter-specific meaning, but none is needed because the lone semantic parameter is fully documented in the schema. 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 names a specific mode (grounded, hallucination-resistant) with a clear verb ('answer'/'extracts') and resource (Pipeworx). It explicitly differentiates itself from ask_pipeworx, establishing what makes this variant distinct: extraction is limited to the tool result. An agent can easily recognize when this tool is the right one.

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 provides explicit when-to-use guidance: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' with concrete examples like financial verdicts and legal claims. It also names the alternative (ask_pipeworx) and states the trade-off ('Costs one extra LLM call... prefer ask_pipeworx for casual lookups').

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