mean

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It says nothing about edge cases: what happens with an empty dataset, whether non-numeric values are rejected, whether the result is rounded, or how it handles extreme values. The only behavioral inference available is that it's a pure read/compute operation, which is implicit in the name, not in the description.

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, front-loaded sentence with no filler or redundancy. It is appropriately sized for the simplicity of the tool, though its brevity comes at the cost of completeness.

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?

The tool has an output schema, so the description needn't explain return values. However, with no annotations, zero schema description coverage, and no behavioral or usage context, the description is too thin for a tool sitting among many similar statistical and arithmetic siblings. It lacks the information an agent would need to confidently select and invoke it correctly in ambiguous scenarios.

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 schema does not document the single 'numbers' parameter beyond its type and title. The description says 'a dataset' but does not clarify that the parameter is a flat array of numbers, whether it must be non-empty, or any constraints. With the schema providing no semantic description and the tool description also silent, the parameter is effectively undocumented.

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 states a specific verb ('Calculate') and resource ('arithmetic mean of a dataset'), which is clear on its own. However, with siblings like geometric_mean, harmonic_mean, median, and mode, the description does nothing to distinguish arithmetic mean from those alternatives beyond the word 'mean' itself. It's a vague but functional purpose 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 when-to-use guidance, no mention of alternatives like geometric_mean or harmonic_mean, and no conditions under which this tool should be selected over its siblings. With 23 sibling tools in the same numeric-calculation domain, the lack of routing guidance is a significant gap.

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