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

No annotations are provided, so the description carries the full burden. It does disclose the computed outputs, and the nature of the operation (pure computation, no mutation implied) is low risk, but it says nothing about edge cases such as what happens with a single data point (stdev undefined) or invalid/mixed input.

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?

A single front-loaded sentence with no filler, and the output list is placed immediately after the purpose. It is efficient, though it trades away usage guidance for brevity.

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 and no output schema, the description partially compensates by enumerating return values, which is genuinely useful. However, it omits minimum-data-size behavior, non-numeric input handling, and any hint of return shape (object vs keyed fields), leaving gaps for an agent to resolve.

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 is a nested object ('payload.data'), so the schema alone leaves the agent guessing at intent. The phrase 'for a list of numbers' maps loosely to the data array and implies numeric-only input, but the payload wrapper and the minItems=1 constraint are never explained in the description.

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 gives a specific verb+resource ('Descriptive statistics for a list of numbers') and enumerates the computed outputs (count, mean, median, stdev/variance, min, max), so an agent knows exactly what it returns. It does not, however, explicitly distinguish itself from the sibling stats_percentile, stats_correlation, or stats_regression tools.

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 when-to-use guidance and no mention of alternatives such as stats_percentile or stats_correlation. The agent must infer from sibling names alone which stats tool fits a given request.

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