plot_box

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 disclosing behavioral traits. It states the tool visualizes outliers and compares distributions, but does not say whether the plot is displayed, saved, or returned, nor whether it mutates any dataframe state. For a plotting tool with no structured safety metadata, this is a significant transparency gap.

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 compact and front-loaded, with the core purpose in the first sentence. It consists of three sentences and an example, with no redundant fluff. The example partially repeats the prose but also illustrates a concrete invocation, making it useful.

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?

With no output schema and no annotations, the description should provide more complete context. It covers purpose and grouping, but does not explain df_name and save_path, nor the display/save behavior or return value. In the context of many sibling plot tools, it also lacks differentiation, leaving notable gaps for correct use.

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 for missing parameter details. It explains 'column' and 'by' via prose and example, but completely omits 'df_name' and 'save_path', leaving half of the parameters undocumented in both schema and description. This partial coverage is insufficient for reliable invocation.

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 clearly identifies plot_box as a box plot for outlier visualization and comparing distributions across groups, with optional grouping by a categorical column. It also explicitly notes it complements detect_outliers, but it does not differentiate from other distribution/grouping plots like plot_violin or plot_qq. Thus it is clear in purpose but lacks full sibling differentiation.

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 use for outlier visualization and group comparisons, and mentions it complements detect_outliers with a visual perspective. However, it gives no explicit guidance on when to use this tool versus other plotting alternatives, nor does it state exclusions or prerequisites. The example shows usage but does not aid tool selection beyond the implied context.

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