real_or_sampling

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It describes the conceptual behavior (checking denominator shifts) but does not specify the output format, assumptions, or edge cases. The agent knows the goal but not the result type.

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 extremely concise, consisting of two short sentences. It front-loads the core question and immediately provides actionable context. Every word earns its place, and there is no waste.

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?

Given the absence of an output schema and annotations, the description should ideally specify the return value. It adequately explains the use case and parameter relationships but leaves a notable gap regarding what the tool returns, which is essential for an agent to use it 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?

Schema coverage is 100%, providing basic descriptions for both parameters. The tool description adds meaningful context by linking parameters to real-world rate metrics and denominator shifts, enhancing understanding beyond the raw schema.

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 states the tool's purpose: to determine if a change in a rate metric is due to actual movement or a shift in the denominator. It uses a specific verb 'run' and resource 'rate metric change'. The description effectively distinguishes from sibling tools like 'ab_test' and 'did_it_change' by focusing on the artifact of denominator shifts.

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 provides explicit guidance on when to use the tool: 'Run this before reporting any RATE as a change', with examples like conversion rates and error rates. It implies the context for use but does not explicitly mention when not to use it or list alternatives; however, the context is clear enough.

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