A deterministic AST evidence engine that forces AI agents to debug using verified execution facts instead of pattern-matching symptoms, enabling hallucination-free debugging for MCP-compatible agents.
An AI debugging agent MCP server that enables autonomous plan-act-observe debugging workflows, allowing repository exploration, code inspection, human-approved edits, and test execution through structured MCP tools.
A local MCP server that helps AI agents recover missing historical developer context by capturing and indexing screenshots with OCR and embeddings into a local SQLite database, enabling grounded question-answering with inspectable event citations.
An MCP server that enables AI agents to autonomously test, debug, and analyze web interfaces visually using Playwright, with 30 tools for screenshots, workflows, performance, and visual comparison.
A local-first MCP server that gives AI coding agents runtime visibility and AI-managed debug logging. It replaces blind print() debugging by turning runtime execution into causal chains, allowing agents to instantly locate bugs by finding missing .success events in Python and TypeScript code. Single binary with MCP, CLI, and HTTP interfaces.
MCP server bridging Lauterbach TRACE32 debuggers to AI agents for autonomous debugging, providing 47 tools for execution control, breakpoints, memory, registers, variables, and symbol inspection.
