MCP server for debugging Node.js programs through the V8 Inspector Protocol, allowing AI agents to set breakpoints, inspect variables, and step through code.
A source-aware MCP server that connects AI agents to browser and server runtimes, enabling real-time debugging, monitoring, and automatic fixes via WebSocket or HTTP.
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.
MCP server that reduces AI agent token usage by up to 90% through intelligent context compression. Enables efficient code exploration, multi-file refactoring, and debugging by providing tools for smart reading, searching, and managing code context.
A FastMCP server that provides LLMs with structured access to Scalene's CPU, GPU, and memory profiling for Python applications. It enables automated performance analysis, bottleneck identification, and optimization suggestions through natural language interactions in supported IDEs.
An MCP server that converts Windows WPR .etl performance traces into structured JSON summaries and flamegraph-ready data for LLM analysis. It bridges Windows Performance Analyzer automation with LLM reasoning capabilities for performance troubleshooting.