mean

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

No annotations are provided, so the description carries full burden. It describes the calculation method but does not disclose behavioral traits such as error handling (e.g., empty lists, non-numeric strings), performance characteristics, or output format. The description is functional but lacks operational context.

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 front-loaded with the core purpose and includes a brief mathematical explanation and domain tags. It is efficient with no wasted sentences, though the parenthetical domain note could be integrated more smoothly.

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 no annotations, 0% schema coverage, and no output schema, the description is incomplete. It explains the calculation but misses critical context: parameter semantics (string array vs. numbers), error conditions, and output details. For a tool with undocumented parameters, this is inadequate.

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 compensate. It mentions 'a list of numbers' but does not clarify that the input schema expects an array of strings, not numbers, nor does it explain formatting (e.g., numeric strings, delimiters). This gap leaves parameter meaning ambiguous beyond the schema.

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 explicitly states the verb ('calculate') and resource ('arithmetic mean (average) of a list of numbers'), with a clear mathematical definition ('sum all values and divide by count'). It distinguishes from siblings by specifying the statistical domain, unlike many other mathematical tools in the list.

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 implies usage for statistical averaging but does not explicitly state when to use this tool versus alternatives like 'median' or 'harmonic_mean' (both siblings). It provides domain context ('statistics, Category: general') but lacks explicit guidance on scenarios or exclusions.

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