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

Annotations already convey read-only, idempotent, non-destructive, and open-world traits. The description adds substantial behavioral context beyond that: it details the success return shape, the exact refusal_reason enum values, that evidence is a verbatim quote, and the cost tradeoff of one extra LLM call. This fully discloses failure and success behavior.

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 clause serves a purpose: mode identity, routing scope, extraction behavior, return contract, refusal reasons, usage contexts, and cost comparison. Slightly longer than minimal, but not padded; front-loaded with the most important distinction (hallucination resistance).

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?

There is no output schema, so the description fully compensates by specifying the response structure with evidence and refusal_reason variants. It explains when to use vs. the cheaper sibling, covers routing breadth, and details failure modes. For a complex tool with this scope, nothing an agent needs to invoke it correctly 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%, so the input schema fully documents the single required 'question' parameter and its aliases. The description does not add parameter-level detail, but the schema carries that burden completely; 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?

States a specific function: a 'Hallucination-resistant answer mode' that routes like ask_pipeworx but extracts answers only from fetched tool results. Explicitly distinguishes it from the sibling ask_pipeworx by the grounded extraction mechanism, so an agent can tell them apart immediately.

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?

Gives explicit when-to-use guidance: 'whenever an answer will be quoted, cited, or acted on' and lists high-stakes domains (financial verdicts, legal claims, medical lookups, public statements). Also names the alternative for casual lookups: 'prefer ask_pipeworx for casual lookups.' Clear selection criteria are fully stated.

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