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

With no annotations, the description carries the full behavioral burden. It usefully discloses the pricing/auth model ('5 free trial calls per signed wallet') and the exact formula, but says nothing about minimum input length (skewness needs at least 3 values), behavior on degenerate input, or the return shape.

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?

One tight sentence with the core operation front-loaded, followed by a short pricing note. Nothing is padded, though the emoji/trial clause is marketing rather than functional content.

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?

Adequate for a simple single-parameter statistic with no output schema: the formula and cost model are covered. It falls short on input constraints (minimum length, array format) that an agent needs to call it correctly the first time.

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 0% and the single parameter 'values' is typed only as 'string' with no description. The description's phrase 'of an array' partially compensates by signaling the string should carry a numeric array, but it gives no delimiter or format guidance.

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?

States a specific verb and resource ('Computes sample skewness of an array') and even names the method ('adjusted Fisher-Pearson formula'), which is more than most statistical siblings offer. However, it never distinguishes itself from the near-identical sibling 'x402-skewness', leaving the sample-vs-population choice to inference.

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 when-to-use guidance, no prerequisites, and no reference to the competing 'x402-skewness' or 'x402-kurtosis-sample' tools. The agent is left to guess which skewness variant applies to its data.

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