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

Annotations already declare readOnlyHint and idempotentHint. The description adds the dependency 'requires matplotlib,' which is useful environment context, but does not describe the return value or side effects (e.g., whether the plot is displayed or saved). This is a modest addition beyond annotations.

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 two sentences plus two compact example calls. It front-loads the purpose and avoids filler, making it easy to parse.

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 modest complexity and full schema coverage, the description sufficiently conveys the core purpose and example usage. It lacks return-behavior details, but the absence of an output schema and the presence of annotations reduce the need for more.

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 has 100% coverage, describing all 5 parameters with types and examples. The description's examples reinforce usage of data_groups and group_labels but do not add meaning beyond the schema's existing parameter descriptions.

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 ('Create') and resource ('box plot') with a clear use case ('comparing distributions'), distinguishing it from sibling plot tools like plot_histogram and plot_scatter by the chart type.

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 phrase 'for comparing distributions' implies the intended use case, but the description does not explicitly state when to prefer this over alternatives (e.g., plot_histogram) or provide exclusions. Examples show invocation but not selection guidance.

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