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 it discloses nothing: not the expected input encoding, whether the mean is arithmetic, whether empty input errors, nor the return shape. 'Mean of of' conveys no behavioral trait at all.

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 text is not concise but broken: 'Mean Of: Mean of of.' contains a duplicated, nonsensical fragment and no front-loaded, actionable information. There is no structure to evaluate.

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 statistics tool with one parameter, no annotations, and no output schema, the description supplies none of the missing context — not the input format, not what the result looks like, not the relationship to sibling mean tools. It is wholly inadequate.

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 description coverage is 100% with a single optional 'values' parameter, so per the baseline the schema nominally documents it. However, the schema text 'Values to process' is generic and the description adds nothing about encoding (comma-separated string? JSON array?), so the tool still leaves the input format ambiguous.

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 reads 'Mean Of: Mean of of.', which merely restates the tool name and then degenerates into a garbled phrase. It gives no verb+resource specificity and does nothing to distinguish this from the many mean-related siblings (x402-geometric-mean, x402-harmonic-mean, x402-weighted-mean, x402-sample-mean, x402-population-mean). Tautology.

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 no guidance on when to use this tool versus any of the many mean/variance/statistics siblings. No preconditions, exclusions, or alternatives are mentioned.

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