MCP server for profiling Java applications via JDK utilities (jcmd, jfr, jps). Enables AI assistants to diagnose performance, analyze threads, and inspect JFR recordings without manual CLI usage.
Provides comprehensive monitoring and observability for MCP server ecosystems with real-time health checks, performance metrics, distributed tracing, anomaly detection, and automated performance reports using OpenTelemetry and Prometheus.
An MCP server that runs parallel AI agents to analyze React components for performance issues, including unnecessary re-renders, memoization errors, bundle size problems, and profiling concerns.
MCP server for analyzing Ascend PyTorch Profiler performance data. Enables users to identify performance bottlenecks, analyze operator time, view communication overhead, and query trace data via natural language.