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.
Bridges AI models with WinDbg to analyze Windows crash dumps and perform remote debugging through natural language queries, enabling execution of debugger commands and automated crash analysis.
Enables natural language analysis of Perfetto traces to diagnose Android app performance issues like ANRs, jank, CPU hotspots, memory leaks, and lock contention without writing SQL queries.
Enables AI assistants to interact with the WinDbg GUI through the Model Context Protocol using a PyKD-based plugin integration. It supports executing debugger commands, inspecting registers, reading memory, and performing automated crash analysis via natural language.
Enables AI agents to analyze Windows crash dumps by providing structured data on exceptions, threads, modules, and source context, with safe patch, build, and test execution.
