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 input format (the 'values' parameter is typed as a string, implying a serialized list), not how outliers or ties are handled, not the output shape. A circular definition of the metric is the only content.

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 brevity here is under-specification rather than economy: the one sentence is a redundant restatement of the name and contributes no actionable information. There is nothing front-loaded to prioritize.

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 no annotations, no output schema, and a string-typed numeric input, the description is far too thin. It never clarifies the input serialization or the computational definition, so an agent has no basis for invoking it 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?

With a single parameter at 100% schema description coverage, the baseline of 3 applies; the schema's 'Values to process' is already the main source of meaning. The description adds nothing about the string encoding (comma-separated? JSON array?), which is the one ambiguity an agent would actually need resolved.

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 almost verbatim: 'Interquartile Mean: Mean of interquartile.' It never defines the statistic (the mean of values between Q1 and Q3, excluding outliers), so an agent gets no operative explanation of what the tool actually computes. This is effectively a tautology, not a specific verb+resource statement.

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, and no reference to the many closely related siblings it competes with (x402-trimmed-mean, x402-winsorized-mean, x402-median, x402-mean-of). An agent cannot infer from the description which summary statistic to pick.

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