plot_violin

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

With no annotations, the description carries full responsibility for behavioral disclosure. It explains the visual output nature but does not mention side effects, whether it displays or saves the plot, or any requirements like a current dataframe. This is a significant gap for a plotting tool.

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 extremely concise, using two short sentences plus a relevant example. No filler, and key information is front-loaded.

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 has 4 parameters and no output schema or annotations. The description does not explain return values, output format, or effects like file saving. It is too minimal to fully inform an agent how to use the tool correctly in all scenarios.

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 description only partially compensates by showing an example with 'column' and 'by'. The parameters 'df_name' and 'save_path' are never mentioned, leaving their meaning unclear. The agent must infer them from names alone.

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 states what the tool does: 'Violin plot: combines box plot with KDE to show full distribution shape.' It uses a specific verb and resource, and distinguishes itself from siblings like plot_box by noting it is 'Better than boxplot for skewed or multimodal distributions.'

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 explicitly provides usage guidance by comparing with alternatives ('Better than boxplot for skewed or multimodal distributions') and gives a concrete example. This tells the agent when to choose this tool over plot_box and how to invoke it.

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