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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false), the description reveals crucial behavior: it makes an extra LLM call, it returns either a success envelope with evidence/refusal_reason:null or an explicit refusal object with enumerated refusal_reason values. It also explains the extraction constraint: 'EXTRACTS the answer using ONLY what the tool result contains.' This is a complete behavioral contract.

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: it states mode, routing, extraction semantics, return contract, use cases, and cost tradeoff. It is slightly long but structured with a clear progression, and key guidance is front-loaded. Only minor redundancy with the 'same routing as ask_pipeworx' phrase when the whole description is already about grounding.

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 compensates by fully specifying the success response shape and all failure refusal_reason values. It also explains the one extra-cost difference from the sibling, which is exactly what an agent needs to decide correctly. Nothing important 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 already documents the single ‘question’ field and all its aliases (query, q, prompt, text, input). The description does not add parameter-level semantics, but that's fine because the schema fully covers it. 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 states a specific verb ('ask') and resource ('Pipeworx') in a distinct grounded mode, and explicitly contrasts it with sibling ask_pipeworx by highlighting 'hallucination-resistant answer mode' and 'same routing as ask_pipeworx—then extracts the answer using ONLY what the tool result contains.' This clearly differentiates it from all sibling tools.

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 explicitly says when to use: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements).' It also gives a clear when-not: 'prefer ask_pipeworx for casual lookups.' This is exceptional usage guidance.

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