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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered by structured data. The description adds only the 'non-empty' precondition, which the schema also states, and nothing about determinism, empty-input errors, or handling of non-numeric values. Adequate but thin for an annotated pure-compute tool.

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?

Two compact sentences with the definition front-loaded and a worked example that doubles as a sanity check. No filler.

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?

An output schema exists, so return-value explanation is unnecessary, and the tool is a straightforward one-parameter computation. The remaining gap is the lack of disambiguation from calculator_average, which matters given the very large sibling set.

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% and the single parameter is documented as 'Non-empty list of numbers.' The description's [10,20,30] → 20 example illustrates the mapping and echoes the schema example, but adds no syntax or edge-case semantics beyond that. Baseline 3 when the schema does the work.

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?

States a specific operation and resource: 'Arithmetic mean of a non-empty dataset.' An agent knows exactly what it computes. However, it never distinguishes itself from the close sibling calculator_average (or median/mode), so differentiation is left to the name alone.

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, no exclusions, and no mention of alternatives. With calculator_average sitting right next to it in the sibling list, the description does nothing to help an agent choose between mean and average, or mean vs. median/mode for skewed data.

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