plot_distribution_comparison

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

No annotations are provided, so the description carries the full burden. It does not disclose behavioral details such as whether the plot is displayed or saved (save_path parameter), how missing values are handled, or any side effects. The example shows usage but does not describe output or edge cases.

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 three sentences, front-loaded with the core purpose, and includes a concrete example. Every sentence contributes value without redundancy or fluff.

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 5 parameters, no annotations, no output schema, and many sibling plot tools, the description is too sparse. It covers the main purpose and one example but omits important usage details like group limiting (top_n_groups) and file saving, making it incomplete for a complex tool.

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?

The schema has 0% description coverage, so the description must compensate. The example clarifies the 'column' and 'by' parameters, but 'df_name', 'save_path', and 'top_n_groups' remain unexplained. This is a significant gap for a 5-parameter tool.

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 a specific action ('Overlay KDE distributions') on a specific resource ('numeric column') with a grouping dimension. It distinguishes itself from sibling plot tools by explicitly positioning itself as better than separate histograms for comparing distribution shapes across categories.

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 provides a clear comparative guideline ('Better than separate histograms for comparing distribution shapes across categories') that helps decide when to use this tool. It does not explicitly mention alternative tools like violin or box plots, but it gives enough context for selection.

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