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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds valuable behavioral detail: hourly recomputation, occasional long latency, possible error='recomputing', and cache warming on retry. This goes well beyond the annotations and prepares the agent for real-world behavior.

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 well-structured, front-loading the core outcome definition, then explaining the honest methodology, then noting the recompute behavior. Each sentence adds value and is tightly written without redundancy.

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?

With no parameters and no output schema, the description covers the essential context: what is measured, how the metric is defined, why it is trustworthy, and operational notes about recomputation. An agent has enough to call and interpret this tool correctly.

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?

There are zero parameters, so the schema is empty. The description does not need to add parameter semantics, and the baseline for zero-parameter tools is 4. It does not discuss parameters because none exist, which 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 purpose: reporting forward-measured outcomes (doubled, 5x, 10x, never moved) for a logged token population. It clearly differentiates itself from a track record or win rate, making the tool's role unambiguous.

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 explains what the tool is not (not a track record, not a win rate) and frames its usefulness for citing honest population-level outcomes. It does not explicitly mention sibling tools or when to choose this over alternatives, but the context is clear enough for an agent to infer its niche.

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