mean

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

With no annotations, the description carries the full burden, and it does disclose the success response shape and error shape, which is genuinely useful. However, it says nothing about edge cases such as empty lists, non-numeric input, or how the mean handles large inputs, leaving behavioral gaps.

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 well-structured and front-loaded with the purpose, followed by Args, Returns, and two short examples. It is appropriately sized; the second example is slightly redundant but harmless.

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 simple single-parameter math operation with no output schema, the description covers input meaning, return format, and error format, which is nearly everything an agent needs to invoke it correctly. Missing edge-case behavior keeps it from a 5.

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%, but the single parameter is named and explained as 'a list of numerical values,' which matches the schema's array-of-number type. This adds little beyond the schema itself, so a baseline 3 is appropriate for one well-typed parameter.

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 and resource: 'Computes the mean of a list of numbers.' It is unambiguous what operation is performed, though it does not differentiate itself from close siblings such as median, variance, or standard_deviation, which it could easily be confused with.

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 on when to use this tool versus the many related statistical siblings (median, variance, standard_deviation, summation). No conditions, prerequisites, or exclusions are given; the agent must infer that a list of numbers is required input.

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