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

No annotations are provided, so the description carries the full behavioral burden and delivers almost nothing. It does not state whether the operation is a pure calculation, what the output looks like, or how it handles empty/short samples, though the deterministic math nature is at least implied by the name.

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?

It is short, but this is under-specification rather than concision: the sentence duplicates the tool name instead of using the space to convey any operationally useful information.

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 one-parameter math utility with no annotations and no output schema, the description should at minimum explain the expected input format for the values string. That gap makes the definition too thin for an agent to call the tool confidently.

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% and the single parameter is documented, so the baseline is 3. The description adds nothing about how the string-typed "values" should be formatted, which is the one thing that would help beyond the 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?

"Sample Mean: Mean of sample" is essentially a restatement of the tool name rather than a definition. It does identify the resource (the sample mean statistic) but adds no distinguishing detail from siblings like x402-population-mean, x402-mean-of, or x402-trimmed-mean.

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 is given. In a list containing population-mean, mean-of, harmonic-mean, geometric-mean and trimmed-mean, the agent gets no help deciding when the sample mean is the right choice versus those alternatives.

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