Debug FAQs

Question 1

How does Debug-MCP integrate with AI assistants like GitHub Copilot?

Accepted Answer

It features an MCP Server, providing an official SDK-based integration for AI assistants. This allows tools like GitHub Copilot to leverage Debug-MCP to set breakpoints, inspect local variables, and control code execution directly within your workspace.

Question 2

Can I use Debug-MCP programmatically or interactively?

Accepted Answer

Absolutely. Debug-MCP offers dual interfaces: a powerful CLI for interactive debugging with clear table output, and a clean Python API for programmatic integration, making it versatile for both manual and automated debugging tasks.

Question 3

What is Debug-MCP primarily designed for?

Accepted Answer

Debug-MCP is a Python debugging tool that provides breakpoint-based debugging capabilities through a clean API and CLI. It's specifically designed for seamless integration with AI assistants and various development tools to enhance coding workflows.

Question 4

What core debugging technologies and features does Debug-MCP utilize?

Accepted Answer

Debug-MCP leverages Microsoft's debugpy via the Debug Adapter Protocol (DAP) for advanced, production-ready debugging. This enables true step-in/over/out execution, breakpoint debugging, isolated debug session management, and environment-aware execution.

Question 5

Is Debug-MCP safe for executing code, and what are its limitations?

Accepted Answer

Yes, Debug-MCP ensures safety through sandboxed subprocess execution, path validation, and working directory lockdown. It includes configurable limits for execution timeout, output capture, variable depth, and collection size. However, users should always debug trusted code.

Debug

Debug

주요 기능

사용 사례

주요 기능

사용 사례