Ci1t Mcp

Provides 20 tools for working with the CI-1T prediction stability framework, focused on detecting anomalies, evaluating model drift, and monitoring prediction fleets at scale. You'd reach for this when running machine learning systems in production and need to catch when models start behaving unexpectedly or degrading over time. The ghost detection likely flags phantom patterns or spurious correlations, while drift evaluation tracks distribution shifts between training and inference data. Designed for teams managing multiple deployed models who need programmatic access to stability metrics and anomaly signals without building their own monitoring infrastructure from scratch.