A Model Context Protocol server that bridges AI models with WinDbg for crash dump analysis and remote debugging.

This MCP server integrates with CDB to enable AI models to analyze Windows crash dumps and connect to remote debugging sessions using WinDbg/CDB.

An AI-powered tool that bridges LLMs with WinDbg for crash dump analysis and live debugging. Execute debugger commands through natural language queries like "Show me the call stack and explain this access violation".

Not a magical auto-fix solution. It's a Python wrapper around CDB that leverages LLM knowledge to assist with debugging.

• Crash Dump Analysis: Examine Windows crash dumps
• Live Debugging: Connect to remote debugging targets
• Directory Analysis: Process multiple dumps for patterns

• Windows with Debugging Tools for Windows or WinDbg from Microsoft Store.
• Python 3.10 or higher
• Any MCP-compatible client (GitHub Copilot, Claude Desktop, Cline, Cursor, Windsurf etc.)
• Configure MCP server in your chosen client

pip install mcp-windbg

The MCP server supports multiple transport protocols:

Standard I/O (default):

mcp-windbg
# or explicitly
mcp-windbg --transport stdio

Streamable HTTP:

mcp-windbg --transport streamable-http --host 127.0.0.1 --port 8000

Endpoint: http://127.0.0.1:8000/mcp

--transport {stdio,streamable-http}  Transport protocol (default: stdio)
--host HOST                              HTTP server host (default: 127.0.0.1)
--port PORT                              HTTP server port (default: 8000)
--cdb-path PATH                          Custom path to cdb.exe
--symbols-path PATH                      Custom symbols path
--timeout SECONDS                        Command timeout (default: 30)
--verbose                                Enable verbose output

To make MCP servers available in all your workspaces, use the global user configuration:

1. Press F1, type > and select MCP: Open User Configuration.
2. Paste the following JSON snippet into your user configuration:

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

This enables MCP Windbg in any workspace, without needing a local .vscode/mcp.json file.

For scenarios where you need to run the MCP server separately (e.g., remote access, shared server, or debugging the server itself), you can use the HTTP transport:

1. Start the server manually:

python -m mcp_windbg --transport streamable-http --host 127.0.0.1 --port 8000

2. Configure VS Code to connect via HTTP:

{
    "servers": {
        "mcp_windbg_http": {
            "type": "http",
            "url": "http://localhost:8000/mcp"
        }
    }
}

Workspace-specific and alternative configuration: See Installation documentation for details on configuring Claude Desktop, Cline, and other clients, or for workspace-only setup.

Once configured, restart your MCP client and start debugging:

Analyze the crash dump at C:\dumps\app.dmp

This server implements the Model Context Protocol (MCP), making it compatible with any MCP-enabled client:

The beauty of MCP is that you write the server once, and it works everywhere. Choose your favorite AI assistant!

Documentation

Analyze this heap address with !heap -p -a 0xABCD1234 and check for buffer overflow"

Execute !peb and tell me if there are any environment variables that might affect this crash"

Run .ecxr followed by k and explain the exception's root cause"

"Connect to tcp:Port=5005,Server=192.168.0.100 and show me the current thread state"

"Check for timing issues in the thread pool with !runaway and !threads"

"Show me all threads with ~*k and identify which one is causing the hang"

Read about the development journey: The Future of Crash Analysis: AI Meets WinDbg

• Reddit: I taught Copilot to analyze Windows Crash Dumps
• Hackernews: AI Meets WinDbg

MIT