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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior, and the description adds substantial extra context: it costs an extra LLM call, returns verbatim evidence, and refuses explicitly with an enumerated list of refusal_reason values. It also states that answers are extracted 'using ONLY what the tool result contains,' which is a meaningful reliability guarantee not present 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 dense but every sentence earns its place: it defines the mode, describes the mechanism, provides the success and refusal output contract, gives explicit usage guidance, and states the cost tradeoff. The critical decision-relevant information is front-loaded in the first sentence.

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 carries the full burden of explaining return values — and it does so thoroughly with the success shape, evidence field, confidence, source, fetched_at, and all refusal_reason variants. It also covers performance cost and usage context, making the tool fully actionable as described.

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%, and the input schema clearly documents the single required question parameter plus its aliases. The description does not add significant parameter-level details beyond saying the tool routes and fills arguments internally, so the baseline of 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 identifies a specific mode — 'Hallucination-resistant answer mode for high-stakes reads' — and clearly distinguishes it from ask_pipeworx by emphasizing evidence-based extraction and explicit refusals. It names the sibling it overlaps with ('Same routing as ask_pipeworx'), so an agent can tell them apart.

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 gives explicit selection criteria: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also names the alternative and the condition that favors it: 'prefer ask_pipeworx for casual lookups.' This leaves no ambiguity about when to choose this tool.

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