MCP server for x64dbg — 152 AI-powered debugging tools. Works with Claude, Cursor, Windsurf, Cline. Reverse engineering with AI.
🤖 An AI-native MCP server for .NET memory dump investigations. Leverage LLMs (Claude/Gemini) to analyze heaps, threads, and exceptions using natural language. Built with ClrMD, token-efficient paging, and Docker support for cross-architecture (.NET/AMD64/ARM64) post-mortem analysis.
AI-Powered Execution Cinema Debugger for Elixir/BEAM
MCP server for rr reverse debugging — enables AI assistants to record, replay, and inspect program executions via GDB/MI
Official Pointbreak debugging plugin for Claude Code - AI-assisted debugging with breakpoints, variable inspection, and step-through execution
Let AI debug your PHP code - MCP server connecting Claude to XDebug for runtime variable inspection, breakpoints, and step-through debugging
🏎️ DRS: Debugging at the speed of F1. An AI-powered Rovo Agent that instantly links Jira incidents to buggy Bitbucket commits.
🔬 Comprehensive Multi-Model Analysis of AI-Driven Debugging Prompts | 13 AI Models Evaluated | Cross-Tier Evaluation Capability Discovered | MIT Licensed Academic Research | v0.1.0
🩺 RAG Doctor — Open-source diagnostic tool for Retrieval-Augmented Generation (RAG) systems. Analyzes codebases to detect architectural issues in LLM pipelines such as missing retrieval, bad chunking, embedding mismatches, and vector database misuse.
🛡️ 17KB justice-driven AI bias detection suite born from fighting for Jahmere Webb's freedom. Real-time bias analysis for chat interfaces, development environments & codebase mapping. <10ms speed, 84.9% accuracy, zero dependencies. Mission: 15% revenue supports criminal justice reform.
AI Visual Debugger - Quickly find and fix programming errors with AI analysis.
Intelligent bug intake and triage assistant that applies rule-based inference and local AI reasoning to structure, classify, and enrich issue reports before engineering handoff.
A machine learning debugging tool that analyzes prediction failures, detects overfitting, imbalance and data leakage, and provides improvement suggestions.
Decision-level observability for LLM pipelines, making system behavior explainable even when no outputs exist.
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