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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses the full refusal behavior, including the specific refusal_reason enum values, and promises verbatim evidence quotes. It also discloses the cost tradeoff of an extra LLM call and that data truncation can trigger a refusal, which is valuable behavioral context 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 front-loaded with the core purpose and then efficiently covers routing mechanics, return shape, refusal semantics, use cases, and cost tradeoff. Every sentence adds decision-relevant information, and the structure flows naturally from what the tool is, to what it returns, to when to choose it.

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: success includes answer, evidence, confidence, source, fetched_at, and refusal_reason:null; failure includes an explicit refusal_reason enum. It also covers when to use the tool and its cost relationship to ask_pipeworx, making the description effectively complete for invocation and expectation-setting.

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?

The input schema already fully documents the question parameter and all its aliases, so the baseline is 3. The description reinforces that the question will be used to select tools and fill arguments, but it does not add new constraints, formats, or examples beyond what the schema already provides.

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 clearly identifies this as a grounded, hallucination-resistant answer mode for high-stakes reads, explaining that it routes through 5,743 tools across 1,500 sources and extracts answers strictly from tool results. It explicitly differentiates itself from the sibling ask_pipeworx by adding an extraction/verification layer, so an agent can immediately tell what this tool does and how it differs.

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 states exactly when to use this tool: 'whenever an answer will be quoted, cited, or acted on' and lists high-stakes domains like financial verdicts, legal claims, medical lookups, and public statements. It also gives an explicit alternative and when-not-to-use instruction: 'prefer ask_pipeworx for casual lookups' and notes the extra LLM call cost.

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