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

Even though annotations already convey read-only, idempotent, non-destructive behavior, the description adds significant behavioral context: it extracts answers 'using ONLY what the tool result contains,' explicitly returns an evidence quote and confidence, and enumerates all possible refusal reasons. It also discloses the extra LLM call cost, which is not visible in 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 longer than average, but every sentence contributes: primary purpose, routing behavior, output format, refusal semantics, precise use cases, and cost-based routing guidance. It is front-loaded with the core differentiator and wastes no words.

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?

For a one-required-parameter tool with no output schema, the description provides a complete operational picture: what happens on success, what happens on refusal, the exact refusal reasons, and why this variant exists. Nothing an agent needs in order to call 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%, and the schema already explains that 'question' accepts query, q, prompt, text, and input as aliases. The description does not add parameter-level meaning beyond that, so the baseline of 3 applies; the schema carries the full weight here.

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'), a specific resource mode ('grounded'), and its defining trait ('hallucination-resistant answer mode for high-stakes reads'). It explicitly contrasts with ask_pipeworx while still clarifying it 'picks the right tool from 5,743 across 1500 sources,' so an agent can distinguish it from its sibling without needing to inspect 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?

The description gives explicit when-to-use criteria: 'Use whenever an answer will be quoted, cited, or acted on' and when the agent 'must not invent facts,' with concrete domains like financial verdicts, legal claims, and medical lookups. It also provides a clear exclusion: 'prefer ask_pipeworx for casual lookups,' and cites the cost tradeoff of one extra LLM call.

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