A
licenseNot graded
qualityC
maintenanceEnables natural-language evaluation, explanation, and reporting of any trained ML model, including performance metrics, prediction insights, drift detection, and PDF report generation.
MIT
DriftScope
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TDQS
A3.5/5.0
Scored across 4 tools
Disambiguation4/5
check_feature_drift and compute_psi both assess feature drift but with distinct statistical methods (KS test vs. PSI) and different outputs (shift detection vs. severity). This is a clear enough separation, though an agent might still pause to choose between them for a generic drift-check request. generate_mock_datasets and get_monitoring_policy are unambiguous.
Naming Consistency5/5
All tools use a consistent verb_noun snake_case pattern: generate_mock_datasets, check_feature_drift, get_monitoring_policy, compute_psi. Minor abbreviation in compute_psi doesn't break the convention. The set is highly predictable.
Tool Count5/5
Four tools are well-scoped for a focused drift analysis server: one data generator, two drift metrics, and one reference tool. Each tool clearly earns its place without redundancy. The count fits comfortably within the ideal 3-15 range.
Completeness3/5
The surface covers continuous feature drift via KS and PSI, plus data generation and policy guidelines. However, it lacks categorical drift tests, batch/multi-feature analysis, and any policy update or model drift tools. These are notable gaps for a server named DriftScope.
Maintenance
ActivityMaintained
ResponsivenessNo issues