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

The description discloses the internal methodology—edge-removed returns and simulated no-edge strategies—and the exact nature of the output, which goes beyond the readOnly/destructive annotations. It also conveys that this is a statistical sanity check rather than a simple evaluation, adding useful behavioral context.

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?

Three sentences, each earning its place: what the tool does, what it returns, and when to use it. The core action is front-loaded and there is no filler or repetition.

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?

An output schema exists, so return-value details are already covered. The description provides enough purpose, method, and usage context for an agent to select and invoke the tool correctly; the only notable omission is explicit guidance about sibling tools, which is already accounted for in usage_guidelines.

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?

With only 50% schema description coverage, the description compensates by explaining the conceptual role of the simulations (pure-luck population with the same length and volatility) and how controls are constructed (edge mathematically removed). It does not map these concepts to parameter names directly, but the schema already documents the selector and simulation fields.

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 names a concrete operation—running Regimen's own analysis against known-answer controls—and specifies both the controls and the two outputs: whether the engine correctly found nothing, and where the real strategy's confidence sits among pure-luck strategies. This clearly distinguishes it from siblings like regimen_evaluate_track_record, which likely judges a track record directly, while this tool attacks the verdict itself.

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 explicitly states when to use the tool: when a user is entitled to ask why they should believe the verdict, or before quoting a result as evidence. It does not name sibling alternatives or provide when-not-to-use conditions, so it stops short of full routing guidance.

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