plot_roc_curve

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

With no annotations, the description carries the full burden. It adds value by explaining AUC semantics and provides a usage example, but it does not disclose side effects like saving to save_path, what the function returns, or behavior if the model or dataframe is missing.

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 concise and front-loaded with the core purpose, followed by a brief explanation of AUC and an example. The AUC interpretation is useful but might be considered extra, though it does not detract from clarity.

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 description gives the purpose, an example, and AUC context, but lacks explanations of all parameters and output/return behavior. With no output schema and no annotations, the description should cover more ground, especially for save_path and target_column.

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 explain all parameters. It only provides an example for model_name and test_df_name, leaving save_path and target_column unexplained. This is insufficient for a 4-parameter tool with no schema 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 clearly states the tool plots an ROC curve with AUC for binary classification models, using the specific verb 'Plots' and a named resource. It differentiates from other plot tools by its focus on ROC/AUC, though it does not explicitly contrast with siblings like plot_precision_recall_curve.

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 indicates the tool is for binary classification models and explains the AUC interpretation, providing clear context for when to use it. However, it does not explicitly state exclusions or mention alternatives, such as using precision-recall curves for imbalanced data.

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