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

No annotations are provided, so the description carries the full disclosure burden, yet it reveals nothing behavioral: no input mechanism (the tool has zero parameters), no output format, no edge-case behavior, no statement about purity or side effects. 'Mean of sample' weakly implies a calculation but discloses nothing an agent needs to predict invocation 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?

At six words the description is technically short, but this is under-specification rather than efficient communication. The content is a near-tautology of the name ('Sample Mean: Mean of sample'), and the label-plus-gloss structure provides no operational information. Every sentence earns its place, but the single sentence earns almost nothing.

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?

With no annotations, no output schema, and an empty input schema, the description is the agent's only source of information and it is critically incomplete. An agent cannot determine how to supply the sample, what the tool returns, what happens on edge cases, or which of the dozens of sibling statistics tools to prefer. This is unusable for correct invocation without external context.

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 per the baseline rule there are no parameter semantics the description must document. However, the description also fails to explain how a parameterless tool obtains its sample, which is a genuine gap — but strictly on the parameter-semantics dimension, the baseline of 4 applies since there is nothing for the schema to have covered.

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 restates the tool name ('Sample Mean' is essentially the human-readable form of x402-sample-mean) and offers only a definitional gloss ('Mean of sample') with no operative verb like 'compute' or 'calculate'. It does not differentiate from siblings such as x402-population-mean, x402-mean-of, or x402-weighted-mean — an agent cannot tell what makes this the right choice.

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 is given on when to use this tool versus the many adjacent statistics siblings (x402-population-mean, x402-harmonic-mean, x402-geometric-mean, x402-trimmed-mean, x402-median). The description provides no context, no exclusions, and no mention of alternatives — only the bare definition.

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