Old Meets New: Bringing Crash Analysis into 2025
Let's face it - while the rest of software development has evolved at warp speed, crash dump analysis feels like it's been preserved in digital amber for decades. We've got self-driving cars and pocket-sized supercomputers, yet here we are, still pecking away at command prompts like it's the dawn of the internet. Why is debugging the only area where we cling to tools that look like they belong in a computer history museum?
Picture this: You, a professional software engineer in 2025, hunched over a terminal, manually typing arcane commands like !analyze -v and .ecxr, squinting at hexadecimal memory addresses, and mentally translating stack traces. All while your friends in other industries are delegating their work to AI assistants that can write entire documents, create art, or automate complex workflows.
Something's wrong with this picture, right?
What if I told you we can throw that ancient workflow into the dustbin of computing history? That's exactly what I've done. And I'm not talking about slightly better syntax highlighting or prettier UI for WinDBG. I'm talking about a fundamental transformation where you simply have a conversation with your debugger.
When Inspiration Strikes
During a debugging session at work, I had one of those lightning bolt moments. What if - and stick with me here - we could apply the same AI-assisted "vibe coding" approach to crash dump analysis?
Picture this: instead of manually slogging through memory dumps and command outputs, you simply ask, "Hey, why did this application crash?" and get an intelligent, contextual answer that actually helps you solve the problem.
The idea was too compelling not to pursue. So I built it.
See It In Action: AI-Powered Crash Analysis
Before diving into the technical details, let me show you what this looks like in practice. I have prepared a demo application to showcase two different use cases:
Video 1: Crash Analysis and Automated Bugfix
In this video, I show how Copilot can analyze a crash dump, identify the bug and auto-fix the issue.
As you can see in the video, instead of manually running WinDBG commands and interpreting the cryptic output, I'm having a natural conversation with GitHub Copilot. The AI quickly identifies that the application crashed, explains which specific conditions led to the crash, and suggests a fix.
Video 2: Automated Crash Dump Analysis of multiple crash dump files
This video demonstrates a different capability: analyzing multiple crash dump files at once. It shows how the tool can quickly identify which dumps belong to your application and which don't.
Worth noting, it takes just a few seconds until you get your first useful answer. I've played around with this for many hours and let me tell you one thing: You can really go deep. If you ask the right questions, the AI runs WinDBG/CDB commands that I haven't seen in all these years of debugging, and that is simply amazing.
How can this help the industry?
I believe this is one of the really good examples of how AI can boost productivity. Analyzing crash dumps is a very tedious task. It begins with quickly checking and identifying whether crashes are the same or different, and often requires very advanced knowledge when a crash is challenging - really challenging.
Copilot can help here tremendously; it knows how to:
- Interpret assembly code (without you having to remember what EAX stands for)
- Check memory contents (so you don't have to count hex bytes on your fingers)
- Traverse structures with symbols (goodbye to manual pointer arithmetic!)
- And so much more
This is a game changer - not just for engineers, but also for support, QA, and everyone involved with crash dumps. It's like going from hunting with a stone spear to using a guided missile.
How did I build this?
If you've ever worked with WinDBG, you know the drill: cryptic commands, obscure syntax, and endless scrolling through memory addresses and stack traces that make your eyes glaze over. It's the kind of specialized knowledge that takes years to master and feels like speaking an alien language even when you do.
The trick here is connecting WinDBG with AI. To do that, you first need to programmatically control a debugging session, right? There are plenty of options on how to do this. I prefer to keep things simple, so I have chosen CDB, which is Microsoft's Console Debugger. It operates on standard input and output, and that's so much more fun to deal with than setting up COM APIs or similar approaches.
The second part is "connecting with AI." That's where Model Context Protocol Servers come into the game.
Understanding Model Context Protocol Servers
MCP is an open standard developed by Anthropic, released in November 2024. This protocol allows AI models to interact with external tools and data sources - think of it as giving AI assistants "hands" to work with other software. It defines a way for AI assistants to discover, access, and use tools through a consistent interface. In essence, it's what allows GitHub Copilot to "talk" to external programs like WinDBG.
An MCP server acts as the intermediary between the AI model and the tool. It:
- Registers available tools with the client
- Handles requests from AI models to use these tools
- Executes the tool operations and returns results
- Maintains context across interactions
This architecture means that any tool can be made available to AI models if someone builds an MCP server for it. And that's exactly what I did for WinDBG (CDB).
Why MCP Instead of LanguageModelTool API?
The LanguageModelTool API might eventually be a better fit for this specific use-case. Creating a Visual Studio Extension that "just works" out of the box would potentially simplify the integration process significantly.
However, using MCP directly offers several notable advantages. It works with any AI model, not just limiting itself to Copilot. The server can be used outside VS Code, functioning with various other tools. New features can be easily added without necessitating changes to the core integration. Moreover, it remains platform-independent, avoiding lock-in to any single company's implementation.
The MCP-WinDBG Project
I've implemented a Model Context Protocol server that wraps WinDBG/CDB and exposes its capabilities to AI models within VS Code. Better yet, I've made it open source so everyone can experience this new workflow.
The project, called mcp-windbg, creates a seamless bridge between VS Code, GitHub Copilot, and the powerful analysis capabilities of WinDBG.
The actual "hard part" was implementing the CDB (Command-Line WinDBG) interaction layer. And by "hard", I mean vibe-coding with two coffees on a Saturday morning, where I spent more time being annoyed by pyTest failures than actual coding difficulties. The core implementation came together surprisingly quickly!
The rest is primarily wrapper code that implements the Model Context Protocol specifications. Now that I've established and defined the core WinDBG interaction logic, I'm considering refactoring the project to TypeScript. This would enable me to create both an MCP Server in TypeScript and a dedicated Visual Studio Extension, with both implementations leveraging the same underlying CDB interaction layer.
What Does This Mean In Practice?
Let me walk you through what this enables:
Natural language crash analysis: "Why is this application crashing with an access violation at this address?" (Instead of: "What the $%#@ is this heap corruption!?")
Contextual debugging: "Show me the stack trace for thread 5 and explain what each function is doing based on the symbols." (Instead of staring at call stacks like they're ancient hieroglyphics)
Root cause identification: "What's causing this null pointer dereference and where should I look in the code to fix it?" (Instead of playing detective with memory addresses)
Instead of typing obscure commands like !analyze -v followed by a series of manual investigations, you simply ask questions in plain language, and the AI interprets the crash data for you. It's like having a WinDBG expert whispering in your ear, except it doesn't get annoyed when you ask the same question five times.
How It Works
The MCP server functions as a bridge between GitHub Copilot and WinDBG's powerful analysis capabilities:
- It provides a set of tools that Copilot can use to interact with crash dumps
- It translates natural language questions into appropriate WinDBG commands
- It parses and interprets the often cryptic WinDBG output into more useful information
- It maintains context throughout a debugging session, enabling follow-up questions to work naturally
The technical implementation uses Python to spawn and communicate with CDB (the command-line version of WinDBG), parses the output, and exposes the functionality through the Model Context Protocol to VS Code.
Getting Started With mcp-windbg
Ready to try it yourself? Here's how to get started:
- First, make sure you have the Windows SDK installed with Debugging Tools for Windows
- Clone the repository:
git clone https://github.com/svnscha/mcp-windbg.git - Set up a Python virtual environment and install the package
- Configure VS Code to use the MCP server
For complete details, check out the repository README.
Once configured, create a .vscode/mcp.json file in your project that points to the server:
{
"servers": {
"mcp_server_windbg": {
"type": "stdio",
"command": "python",
"args": [
"-m",
"mcp_server_windbg"
],
"env": {
"_NT_SYMBOL_PATH": "SRV*C:\\Symbols*https://msdl.microsoft.com/download/symbols"
}
},
}
}
You might need to update the command, depending on where and how you have installed the mcp_server_windbg to.
The Human Touch Still Matters
Just like with code refactoring, the AI assistance isn't perfect. The human element - your experience, intuition, and domain knowledge - remains crucial. Sometimes you'll need to guide the analysis, ask follow-up questions, or provide additional context.
But that's exactly what makes this approach so powerful: it combines the best of both worlds - AI's ability to quickly process and analyze large amounts of data with your human expertise in interpreting what truly matters for your specific application. Think of it as having a brilliant but occasionally confused intern who can do incredible things but sometimes needs you to point them in the right direction. "No, not that pointer... the OTHER pointer."
Join The Experience
I'd love for you to try this out, contribute to the project, and share your experiences. If you're interested:
- Star the GitHub repository
- Try it on your own crash dumps
- Report issues, suggest improvements, or contribute code
- Share your success stories (or even failures - we learn from those too!)
The Magic Is In The Flow
Just like with my code refactoring experience, the real magic isn't about any single capability - it's about the flow. When debugging stops being a tedious chore and becomes a fluid conversation, something fundamentally changes in how you approach problem-solving.
Gone are the days of dreading crash analysis. Instead, each debugging session becomes an opportunity for collaboration with an AI partner that helps you understand what's happening at a deeper level.
Wrapping Up
Crash dump analysis has traditionally been one of the most technically demanding and least enjoyable parts of software development. It's like archaeology with a keyboard—painstakingly excavating through layers of memory and CPU state to unearth what went wrong. With AI assistance through tools like mcp-windbg, it becomes another area where we can experience that perfect "vibe state" of frictionless problem-solving.
If you're still manually typing WinDBG commands and squinting at memory dumps in 2025, you're not just missing out on productivity - you're missing out on a fundamentally more enjoyable way to work.
Try it. Debug it. Vibe it.