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

No annotations are provided, so the description carries the full burden of behavioral disclosure, and it discloses nothing: not the input format expected for 'values', not whether the computation is population or sample skewness, not the output shape, not error behavior on non-numeric input. A statistics tool with zero behavioral context is unusable without trial and error.

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 a degenerate duplication of the title with zero informational content. It is short but not concise in a useful sense, since it fails to front-load any purpose.

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 statistical computation tool with no annotations and no output schema, the description should at minimum specify the expected input encoding and the algorithm variant (sample vs population). None of that is present, leaving the agent unable to call the 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?

Schema coverage is 100% for the single parameter, so baseline is 3 per the rubric. The schema documents 'values' as 'Values to process', which is itself generic, but the description adds no further meaning (e.g., delimiter, JSON array format) beyond it.

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 literally the title repeated: "Skewness Sample: Skewness Sample". It restates the name with no verb, no resource framing, and no explanation of what a skewness sample computation is. Among hundreds of statistics siblings (x402-skewness, x402-kurtosis-sample, x402-sample-variance), this gives no basis for differentiation.

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?

No guidance whatsoever on when to use this tool versus alternatives like x402-skewness, x402-sample-variance, or x402-kurtosis-sample. The agent has nothing to route on.

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