mean

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

No annotations are provided, so the description carries full burden of behavioral disclosure. The description only states what the tool calculates, with no information about error handling (e.g., empty arrays, non-numeric elements), performance characteristics, return format, or computational limitations. For a mathematical tool with zero annotation coverage, this is a significant gap.

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 description is a single, efficient Chinese sentence that directly states the tool's purpose with zero wasted words. It's appropriately sized for a simple mathematical operation and front-loads the essential information.

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?

Given the tool's mathematical simplicity, no annotations, no output schema, and minimal parameter documentation, the description is incomplete. While the core operation is clear, it lacks information about input validation, error conditions, return format, and practical usage considerations that would help an agent invoke 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?

The input schema has 0% description coverage, with only one parameter 'numbers' of type array with unspecified items. The description mentions '数组' (array) but provides no additional semantic information about what the array should contain (e.g., numeric values only), acceptable formats, or constraints. It partially compensates for the schema gap by identifying the parameter type but lacks crucial details.

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 '计算数组的算术平均值' (calculate the arithmetic mean of an array) clearly states the verb ('计算' - calculate) and resource ('数组的算术平均值' - arithmetic mean of an array). It distinguishes from siblings like 'median', 'mode', 'sum', and 'stdDev' by specifying the arithmetic mean operation. However, it doesn't explicitly differentiate from similar statistical tools beyond naming the operation.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose 'mean' over 'median' or 'mode' for central tendency measurement, nor does it specify prerequisites or appropriate contexts. The agent must infer usage from the mathematical operation alone.

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