mode

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

No annotations are provided, so the description bears full behavioral burden. The parenthetical '(s)' hints that multiple modes may be returned, which is useful, but there's no disclosure of edge cases like empty input, all-equal values, or how ties are reported.

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?

One short, front-loaded sentence with no filler. Appropriate size for a simple statistical tool, though it could afford one more sentence of detail without becoming bloated.

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 is simple and has an output schema, so return values needn't be explained. However, for a statistical function with a 0%-documented parameter and no annotations, the description is barely adequate.

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 coverage is 0% and the single parameter 'numbers' is only documented by its property title. The description doesn't clarify the expected array form, numeric constraints, or handling of floats, so it does not compensate for the low coverage. Baseline 3 reflects that one parameter is at least obvious from the name.

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?

States a specific verb ('find') and the statistical concept ('mode(s) of a dataset'), which is enough to distinguish it from siblings like mean, median, and sum. It doesn't explicitly name a sibling but the resource is unambiguous.

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 prefer mode over mean or median, nor any note about what happens with multimodal datasets despite the parenthetical '(s)'. Usage is entirely inferred from the name.

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