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

With no annotations and no meaningful description, the full burden of behavioral disclosure falls on the description, which is absent. The agent is not told whether this computes a statistic, what input it expects, what it returns, or any side effects or edge-case 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 short but this is under-specification, not conciseness. The single phrase repeats the tool name and earns no place; it conveys no additional information and is effectively a placeholder.

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 zero annotations, no output schema, and an empty input schema, the description is the only source of context. It is entirely inadequate: an agent cannot determine what input to provide, what the output means, or even whether this tool computes sample skewness from a data array, a distribution, or something else.

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 has zero parameters, so there is no parameter semantics burden for the description to carry. The baseline of 4 applies because there are simply no parameters to document; the empty schema fully covers the parameter surface.

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 is a pure tautology: 'Skewness Sample: Skewness Sample' merely restates the tool name with no verb, resource, or explanation of what the tool does. It provides no information that would help an agent distinguish it from sibling tools like x402-skewness, x402-kurtosis-sample, or x402-variance-sample.

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?

There is zero guidance on when to use this tool versus alternatives. The description does not mention sample vs population skewness, required input format, or any condition under which this tool should be preferred over closely related siblings such as x402-skewness or x402-sample-variance.

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