svnscha - Profile Picture
🚀 New Release: mcp-windbg 0.10.0
Posted on - ai debugging mcp-windbg copilot crash-analysis community release

mcp-windbg 0.10.0: Now Available on PyPI

Five months after the initial release of mcp-windbg, I'm releasing version 0.10.0 with significant improvements and simplified deployment.

mcp-windbg is now officially available on PyPI. Installation no longer requires cloning repositories or setting up development environments:

pip install mcp-windbg

This represents a significant simplification from the original setup process described in my first article.

Additionally, mcp-windbg is now listed on the official Model Context Protocol registry, which should improve discoverability and enable integrations with various MCP-compatible tools.

Release 0.10.0: Key Features and Improvements

Version 0.10.0 incorporates community feedback and focuses on improving usability across different experience levels with debugging tools.

New Features

Live Debugging Sessions

  • Added open_windbg_remote and close_windbg_remote functions
  • Support for connecting to live processes beyond crash dump analysis
  • Real-time debugging through conversational AI interface

Extended Dump File Support

  • Full compatibility with .mdmp and .hdmp formats
  • Microsoft Store WinDbg CDB compatibility
  • Improved automatic discovery of crash dump files

Development Infrastructure

  • Migrated to uv package manager for improved build performance
  • Significantly reduced development setup and build times
  • Streamlined dependency management

Community Contributions

@sooknarine contributed several key improvements that made this release possible:

These contributions demonstrate the value of community involvement in open source development.

Infrastructure Improvements

Several behind-the-scenes improvements enhance reliability and maintainability:

  • Continuous Integration: Automated testing across Python versions 3.10 through 3.14
  • Dependency Management: Automated security updates and dependency maintenance
  • Release Process: Streamlined deployment for faster delivery of updates

Documentation Updates

The documentation has been restructured for better usability:

  • AGENTS.md: Comprehensive debugging instructions specifically for AI assistants
  • Structured Templates: .github/prompts/dump-triage.prompt.md for consistent crash analysis (inspired by my prompt engineering article)
  • Repository Wiki: All documentation now lives in an easily searchable wiki
  • Simplified README: Focused on getting you started in minutes, not hours
  • This Changelog: So you know what's changing and why

Installation: From Tedious to Trivial

Remember the old installation process? Clone, setup virtual environment, install dependencies, configure paths... ugh.

Here's the new process:

# Install mcp-windbg
pip install mcp-windbg

# That's it. Seriously.

Then add this to your .vscode/mcp.json:

{
	"servers": {
		"mcp_windbg": {
			"type": "stdio",
			"command": "python",
			"args": [
				"-m",
				"mcp_windbg"
			],
			"env": {
				"_NT_SYMBOL_PATH": "SRV*C:\\Symbols*https://msdl.microsoft.com/download/symbols"
			}
		}
	}
}

The installation process is now considerably simplified compared to the previous manual setup requirements.

What's Next?

With 0.10.0 shipped and the foundation solid, I'm excited about what's coming:

  • More AI integrations beyond VS Code
  • Enhanced live debugging capabilities
  • Performance optimizations for large dump files
  • Community-requested features (keep them coming!)

The development roadmap will continue to incorporate community feedback and feature requests.

Getting Started

To begin using mcp-windbg 0.10.0:

  1. Install: pip install mcp-windbg
  2. Configure: Add the MCP server to your VS Code configuration
  3. Use: Load crash dumps and interact through natural language queries
  4. Contribute: Report issues, suggest features, or contribute code improvements

Links:

Community Response and Impact

Since the initial release in May, the project has received significant community engagement. The original article generated over 40,000 page views within the first two days, along with thousands of LinkedIn impressions and direct messages from developers worldwide.

The feedback has been constructive and encouraging. Developers have shared how the tool helped reduce time spent on crash analysis, and several teams have integrated it into their debugging workflows. I've had opportunities to present this work both internally and externally, leading to valuable discussions about AI-assisted development tools.

The open source community response has been particularly gratifying. Contributors like @sooknarine have stepped in to add functionality that benefits all users. The GitHub repository has gained considerable attention, and the project's inclusion in the official MCP registry should further improve its discoverability.

Building something that genuinely helps other developers solve real problems has been rewarding beyond what I expected when I first started working on this as a way to improve my own debugging workflow.

Thanks to everyone who has contributed, shared feedback, or simply tried the tool. The community response has made this project much better than it would have been otherwise.