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

Even though annotations already declare readOnlyHint and idempotentHint, the description adds substantial behavioral context beyond them: the exact success return shape with verbatim evidence, the explicit refusal contract with five enumerated refusal_reason values, the 'uses ONLY what the tool result contains' grounding guarantee, and the extra LLM call cost. This far exceeds what the annotations convey.

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?

Five information-dense sentences, each earning its place: what it is, how it routes, the return/refusal contract, when to use it, and the cost tradeoff. The core purpose is front-loaded and there is zero repetition of annotation or schema content.

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 present, the description fully compensates by specifying the exact return shape and refusal payload. Combined with 100% schema parameter coverage and annotations covering the safety profile, nothing an agent needs to invoke this tool correctly is missing — including the routing behavior and cost implication.

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% — the question parameter and all five aliases (query, prompt, text, input) are fully documented in the schema. The description adds no parameter-level detail, but with full coverage the schema carries the burden adequately, so the baseline 3 applies.

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 states a specific, meaningful mode — 'hallucination-resistant answer mode for high-stakes reads' — and immediately contrasts it with its sibling ('Same routing as ask_pipeworx'), making the distinction between the grounded extraction behavior and the standard mode explicit. An agent can clearly tell this apart from ask_pipeworx and ask_pipeworx_beta 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?

Explicit when-to-use criteria are given ('Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts') with concrete high-stakes examples, and the alternative is named with a clear exclusion ('prefer ask_pipeworx for casual lookups'). The cost tradeoff (one extra LLM call) further helps the agent decide.

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