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

With no annotations, the description carries the full burden; it usefully discloses the cost ('1 credit per call') and the statistical method, which are real behavioral facts. However, it says nothing about validation behavior, error handling, or what a valid call looks like given that zero parameters are required.

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?

Two tight sentences, front-loaded with the primary capability and closing with the cost. Nothing is padded or redundant.

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?

For a 6-parameter, zero-required-parameter statistical tool with no output schema and no annotations, the description leaves critical gaps: it never explains which parameter combination triggers the significance test vs the sample-size calculation, so an agent could easily make a nonsensical call.

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 description coverage is only 33%: a_visitors, b_visitors, a_conversions, and b_conversions have no descriptions in either the schema or the description. The text hints at 'visitors per variant' and 'a given lift', but does not explain the conversion counts or how the two modes map onto the six parameters.

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 two specific capabilities with the underlying method ('two-proportion z-test') for comparing ad or page variants, and sample-size calculation for a target lift. No sibling tool overlaps this statistical domain, so an agent can identify it instantly.

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 implies two usage modes ('compare two variants' vs 'calculate how many visitors...') but never states which parameters select which mode, nor any prerequisites or exclusions. Usage is inferable but not explicit.

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