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Most tools have distinct purposes, but some overlap exists between explain_prediction and explain_prediction_waterfall (both explain individual predictions using SHAP), and between detect_drift and detect_feature_drift (both detect drift, with one being a subset of the other). The descriptions clarify the differences, but an agent might occasionally misselect between these pairs.

Tool names follow a consistent snake_case pattern throughout, with clear verb_noun structures (e.g., compare_features, describe_dataset, detect_drift). There are no deviations in naming conventions, making the set predictable and easy to parse.

With 20 tools, the count is slightly high but reasonable for a comprehensive XAI toolkit covering explanation, drift detection, feedback, and context retrieval. It feels slightly heavy but not overwhelming, as each tool appears to serve a specific function in the domain.

The toolset provides complete coverage for the XAI domain, including model listing, prediction explanation, drift detection, feedback recording, and business context integration. There are no obvious gaps; it supports a full workflow from model discovery to explanation and evaluation.