mean

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

With no annotations, the description carries the behavioral burden; it clearly states that only non-empty inputs are valid and precisely defines the calculation. Since this is a pure mathematical operation with no side effects, no further behavioral disclosure is necessary beyond what the output schema supplies.

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; the verb, operation, and input constraint are all present and nothing is repeated from the schema.

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 one-parameter pure function with an output schema, this description is nearly complete: it states the operation and the non-empty precondition. It only lacks explicit differentiation from median/standard_deviation and edge-case behavior, which are minor given the low complexity.

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 0%, so the description must carry the parameter meaning. It adds a non-empty constraint and the arithmetic-mean operation on the list, but it does not elaborate on element constraints beyond the schema's 'number' type.

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 names a specific operation ('Calculate the arithmetic mean') and a specific input ('a non-empty list'), so an agent can tell it apart from siblings like median and standard_deviation without opening the schema.

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 about when to choose the mean over median or standard_deviation, and no explicit when-not-to-use clause. The phrase 'arithmetic mean' implies a context, but the description does not state it.

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