Provides telemetry, rule-based anomaly detection, and maintenance recommendations for a synthetic connected-vehicle fleet through five narrow MCP tools, enabling fleet monitoring and analysis without external APIs.
An MCP server that exposes GPU-accelerated anomaly detection to AI assistants via the Model Context Protocol. Provides two MCP tools: waveguard_scan (send training + test data in one call, returns per-sample anomaly scores and top explanatory features) and waveguard_health (check API and GPU status). Works on time series, JSON, numbers, text, and images — fully stateless.
Enables incident detection and analysis by identifying anomalies in metric time series and surfacing root-cause candidates and recommended actions. Supports both mock (synthetic) and VictoriaMetrics backends with identical MCP tool contracts for seamless development-to-production switching.
Exposes live industrial IoT telemetry to any MCP client, streaming simulated sensor data from a fleet of machines and detecting anomalies, with the ability to inject faults on demand.