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

Annotations already mark it read-only, open-world, and idempotent, and the description adds substantial non-obvious behavior beyond those flags: the strict 'ONLY what the tool result contains' extraction rule, the exact success/refusal return shapes, enumerated refusal reasons, and the additional LLM call cost. 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: a crisp core claim, the routing mechanics, the return contract, explicit use cases, and the cost/alternative guidance. It is front-loaded with the most decision-relevant qualifier and contains no filler or tautology.

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?

Given there is no output schema, the description fully compensates by specifying the success payload, the refusal payload, the list of refusal reasons, the evidence-verbatim guarantee, and the cost difference from ask_pipeworx. Together with the annotations, an agent has everything needed to select and safely invoke this tool.

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 schema already documents the single semantic parameter and all six aliases. The description does not add parameter-level details beyond implying that the tool 'fills arguments' internally, which is behavior rather than parameter semantics. Baseline 3 is appropriate because the schema does the heavy lifting.

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 opens with a specific, meaningful distinction: 'Hallucination-resistant answer mode for high-stakes reads.' It then explains precisely what the tool does: routes like ask_pipeworx, fetches data, and extracts an answer using only tool-result content, with verbatim evidence. This clearly differentiates it from the sibling ask_pipeworx and other cousins.

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 it: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' with concrete examples. It also gives an explicit alternative: 'prefer ask_pipeworx for casual lookups,' and notes the cost tradeoff of one extra LLM call. This is exemplary routing guidance.

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