Сonnects to live JVMs via JDWP protocol,
enabling LLM agents to autonomously debug Java applications — attach to
a running process, pause threads, set breakpoints (including conditional),
inspect stacks with auto-resolved object fields, evaluate methods, set
variable values, and diagnose issues like deadlocks and hung queries.
Lets AI agents diagnose and debug live Java applications by auto-discovering JVMs and attaching Arthas to run diagnostic commands like thread, trace, watch, and ognl.
Provides AI coding agents with real-time visibility into local development runtime state, enabling them to tail application logs, inspect ports, monitor process metrics, and diagnose network errors.
Bridges AI coding agents with the browser to provide visual debugging, real-time error capture, screenshot capabilities, DOM inspection, and interactive wireframing through a reverse proxy with injected developer tools.
Enables coding agents to connect to Debug Adapter Protocol debuggers over MCP, allowing them to launch or attach to native targets, control execution, set breakpoints and watchpoints, inspect stacks, variables, memory, and exceptions, and capture agent-friendly debugger snapshots.
Exposes a live application's runtime state to coding agents, including logs, DB schema, config, and feature flags, with read-only SQL and secret redaction.
