svnscha - claude-code

🚀 New Release: mcp-windbg 1.3.0

2026-09-10T00:00:00+00:00

Version 1.3.0 of mcp-windbg separates the Claude Code integration into three plugins. If you've already installed mcp-windbg yourself, you can now add the debugging skills and crash-analysis agent to that connection.

Three Separate Plugins

The 1.2.0 release bundled the server, skills, and agent together. I split them so you can choose the parts you need:

Plugin	What it provides
`mcp-windbg-uvx`	The MCP server, launched with uvx, and default symbol settings
`mcp-windbg-skills`	Four debugging skills
`mcp-windbg-agents`	The `crash-analyst` agent

The uvx plugin now ships only the server configuration. Skills and agents are independent: install either, both, or neither.

Skills and Agents for Your Existing Server

If mcp-windbg is already registered in Claude Code, add the marketplace and install the workflows you want:

/plugin marketplace add svnscha/mcp-windbg
/plugin install mcp-windbg-skills@mcp-windbg
/plugin install mcp-windbg-agents@mcp-windbg

The skills cover the same four tasks:

analyze-dump for crash dump analysis.
debug-remote for live user-mode debugging.
kernel-debug for live kernel debugging.
windbg-doctor for checking the debugging setup.

They use your configured MCP connection, whether it runs a native executable, a Python installation, or an HTTP service. Setup diagnosis checks that server's actual launcher and host. It no longer assumes every installation needs uv.

For example:

/mcp-windbg-skills:analyze-dump C:\dumps\app.dmp

The agent is available as mcp-windbg-agents:crash-analyst. It investigates a dump and reports its likely cause, debugger evidence, and next steps. It uses the MCP tools directly and does not need the skills plugin.

If you want the plugin to start the server through uvx, install it as well:

/plugin install mcp-windbg-uvx@mcp-windbg

Windows and CDB/KD are still required on the debugging host. The server's built-in MCP prompts are available independently of these plugins.

Enterprise environments: Managed allowedMcpServers settings can cause Claude Code to silently skip plugin-bundled MCP servers (issue #32882). I recommend manual installation and registration plus the skills plugin, with the agents plugin if needed. The server must still be permitted by your organization's MCP policy.

Upgrading from the Bundled Plugin

Update the marketplace and server plugin:

/plugin marketplace update mcp-windbg
/plugin update mcp-windbg-uvx@mcp-windbg

Then install the skills and agents plugins using the commands above if you want to keep those workflows.

Skill commands now start with /mcp-windbg-skills: instead of /mcp-windbg:. For example, /mcp-windbg:analyze-dump becomes /mcp-windbg-skills:analyze-dump. References to the agent should use mcp-windbg-agents:crash-analyst. Restart Claude Code if the new components do not appear.

See the full v1.3.0 release notes and the plugin guide for installation, switching servers, and updating each plugin.

🚀 New Release: mcp-windbg 1.2.0

2026-08-27T00:00:00+00:00

Version 1.2.0 of mcp-windbg adds a Claude Code plugin. It packages the MCP server together with the instructions and workflows needed for common debugging tasks, so Claude Code can install and use it without a separate Python setup or a hand-written MCP configuration.

Installing the Plugin

Add the mcp-windbg marketplace and install the plugin from Claude Code:

/plugin marketplace add svnscha/mcp-windbg
/plugin install mcp-windbg-uvx@mcp-windbg

The plugin starts mcp-windbg with uvx, which downloads the pinned version from PyPI the first time it runs. You still need Windows, CDB, and uv, but you do not need to install the Python package yourself.

The existing pip install mcp-windbg setup remains available for other MCP clients and for anyone who prefers a manual configuration.

Skills for Common Debugging Tasks

The plugin includes four skills:

analyze-dump guides the analysis of a Windows crash dump.
debug-remote works with a live user-mode target through CDB.
kernel-debug handles live kernel debugging with KD.
windbg-doctor checks the local setup when CDB, KD, symbols, or the MCP server are not working as expected.

These skills only load when they are needed, which keeps the plugin's normal context use small. The underlying MCP tools are still available directly when you want to run a specific command or control a session yourself.

The Crash Analyst Agent

The plugin also includes a read-only crash-analyst agent. It can investigate a dump independently and return:

its likely cause
the debugger evidence supporting that conclusion
alternative causes it checked
practical next steps

The agent is kept away from mutating tools. It can inspect a debugging session, but it cannot resume a target or perform another action that would change its state.

Symbols by Default

Readable stack traces depend on symbols, and missing symbol configuration is a common source of poor results. The plugin now provides the Microsoft symbol server as the default value for _NT_SYMBOL_PATH.

If you already have _NT_SYMBOL_PATH configured, the plugin leaves it unchanged. Existing private symbol servers and custom cache paths therefore continue to work.

A Small 1.2.1 Follow-up

Version 1.2.1 followed later the same day. It reorganized the README installation guide by client and fixed a corrupted C:\dumps\app.dmp example that contained a hidden control character. There were no runtime changes, so it does not need a separate post.

Getting Started

Use the two plugin commands above for Claude Code, or upgrade the Python package for another MCP client:

pip install -U mcp-windbg

See the full release notes for v1.2.0 and v1.2.1. The source and plugin marketplace are available in the mcp-windbg repository.

Ollama Now Speaks Messages API

2026-01-22T00:00:00+00:00

The News: Ollama Learns a New Language

Claude Code can now talk to your locally-hosted models without any extra adapters, proxies, or dark magic.

"Ollama v0.14.0 and later are now compatible with the Anthropic Messages API, making it possible to use tools like Claude Code with open-source models." — Ollama Blog

Cue the excitement: Finally! Unlimited coding on steroids! No more watching your token budget evaporate! 24/7 AI-assisted development without the API bill anxiety!

...right?

Why Test This, You Ask?

Naturally, I had to try this immediately. What better excuse to fire up the DGX Spark and see what these local models can really do?

If you're running a DGX Spark (or any beefy GPU setup), this means you can now fire up Claude Code and have it talk to whatever model you've got running locally. Zero cloud dependency. Zero API costs eating into your coffee budget. Just pure, local AI goodness.

But does it actually work? Let's find out.

Setting It Up

Which Model?

The official docs recommend a few options:

Cloud models (if you're into that sort of thing):

glm-4.7:cloud, minimax-m2.1:cloud, qwen3-coder:480b

Local models (the fun part):

qwen3-coder - Excellent for coding tasks
gpt-oss:20b - Strong general-purpose model
glm-4.7-flash - Deep reasoning, needs Ollama 0.14.3

I tested several of these locally - qwen3-coder is genuinely impressive for coding tasks, and glm-4.7-flash (just released with Ollama 0.14.3) surprised me with its deep reasoning approach. Cloud models? Kind of defeats the purpose of running local, doesn't it?

Bump Ollama's Context Window

The docs recommend at least 32k context. Use systemctl edit for a persistent override that survives updates:

sudo systemctl edit ollama.service

Add this in the editor that opens:

[Service]
Environment="OLLAMA_CONTEXT_LENGTH=32000"
Environment="OLLAMA_FLASH_ATTENTION=1"
Environment="OLLAMA_KV_CACHE_TYPE=f16"

Bonus performance tweaks:

OLLAMA_FLASH_ATTENTION=1 - Normal attention loads the entire context into memory at once. Flash Attention breaks it into chunks, processes them sequentially, and combines the results. Same output, way less memory.
OLLAMA_KV_CACHE_TYPE=f16 - Keeps the K/V cache at full precision (default). Use q8_0 to halve memory if you're tight on VRAM.

How much context can you actually fit? The VRAM Calculator is your friend. Play around with your model size, quantization, and available VRAM - it'll tell you the exact settings to max out each model.

Then restart:

sudo systemctl restart ollama.service

PowerShell Helper for Your Work Machine

This little function lives in my $PROFILE:

function Enter-ClaudeSpark {
   $env:ANTHROPIC_AUTH_TOKEN = "ollama"
   $env:ANTHROPIC_BASE_URL = "http://spark-1:8080"
   $env:CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC = "1"
}

Now I just type Enter-ClaudeSpark and Claude Code magically routes everything to my DGX Spark. The ANTHROPIC_AUTH_TOKEN is set to ollama because Ollama doesn't actually need a real token - it just checks if the header exists. Clever.

My Test: Meeting Destruction Therapy

Public benchmarks do not always reflect how a model feels in everyday use. I care more about how well it follows my language and understands the task, so I made a small practical test of my own.

I needed something creative, visual, and complex enough to separate the wheat from the chaff. What better way than asking these models to build a Breakout-style game where you "reschedule" your weekly meetings by smashing them with a paddle?

The Prompt:

# Breakout-Style Calendar Game Prompt

## Objective
Build a **simple, modern Breakout-inspired browser game** where a bouncing ball clears **calendar-style blocks representing weekly meetings**, and a horizontal paddle is used to “reschedule” (break) them.

## Core Concept
- The playfield should resemble a **weekly calendar view** rather than a classic arcade board.
- Each block represents a **meeting/event** (rounded rectangles, subtle borders).
- The paddle metaphorically “reschedules” meetings by bouncing the ball upward.
- The ball “clears” meetings as they are hit.

## Visual & UX Direction (Very Important)
- **Look & feel inspired by modern calendar apps** such as **Google Calendar** or **Outlook Calendar**:
  - Soft neutral background (light or dark mode acceptable)
  - Rounded corners, subtle shadows, clean typography
  - Muted but intentional color palette (event colors feel like calendar categories)
  - Gentle grid or column structure reminiscent of a weekly view
- Avoid retro/arcade styling (no pixel fonts, neon colors, or harsh outlines).
- Animations should feel **smooth and calm**, not frantic:
  - Subtle easing on ball movement
  - Soft fade/scale effects when meetings are cleared
  - Light hover or focus states where appropriate

## Technical Requirements
- Use **only vanilla HTML, CSS, and JavaScript** (no frameworks or libraries).
- Single-page implementation.
- **Responsive and centered layout** that works on desktop and mobile.
- Canvas or DOM-based rendering is acceptable, but visuals should remain crisp.
- Code should be clean, readable, and well-commented.

## Gameplay Requirements
- Paddle controlled via mouse/touch (keyboard optional).
- Ball physics should feel smooth and predictable.
- Winning state when all meetings are cleared.
- Optional: subtle sound effects or score labeled as **“Meetings Cleared”**.

## Polish Encouraged
- Thoughtful spacing and alignment
- Calendar-like typography (system UI fonts preferred)
- Micro-interactions and transitions that reinforce the scheduling metaphor

## Implementation
- Create index.html, style.css and game.js.

When finished, suggest that the user can host and test the game locally using:

python -m http.server

I threw this at several models through Claude Code and timed the results:

Model	Time	Rating
Claude Opus 4.5	2m 30s	⭐⭐⭐⭐⭐
`qwen3-coder:30b`	1m 48s	⭐⭐⭐⭐
`gpt-oss:20b`	4m 24s	⭐⭐⭐
`glm-4.7-flash`	8m 45s	⭐⭐⭐
`nemotron-3-nano:30b`	2m 46s	⭐
`ministral-3:14b`	-	🚩
`rnj-1`	-	🚩

Claude Opus 4.5 (Reference)

Not self-hosted - this is the cloud-based frontline model, included for comparison. Understood the creative brief, nailed the game mechanics, produced clean and maintainable code. No surprises there.

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qwen3-coder:30b

Faster than expected, and it actually produced something playable. At 30B parameters, genuinely impressive.

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gpt-oss:20b

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For this one I went all out - maxed context window at 95k with full precision KV cache:

Environment="OLLAMA_CONTEXT_LENGTH=95232"
Environment="OLLAMA_FLASH_ATTENTION=1"
Environment="OLLAMA_KV_CACHE_TYPE=f16"

Solid result! The 20B variant delivered a working game with proper paddle controls. Not as polished as qwen3-coder, but definitely playable. A good middle-ground option if you want something from the GPT family.

glm-4.7-flash

"As the strongest model in the 30B class, GLM-4.7-Flash offers a new option for lightweight deployment that balances performance and efficiency." - Bold claims require testing. This one needs Ollama 0.14.3 (just released!).

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Why the 8+ minute runtime? GLM spends serious time in its thinking stage. Watch the Ollama output live and you'll see deep, methodical reasoning - no "what if" self-doubt loops, just clear and consistent problem-solving. The result shows: best physics of all the OSS models I tested. The ball movement felt smooth and responsive. The catch? It completely ignored the meeting-themed UI brief and went with a generic breakout style instead. So close, yet so far.

nemotron-3-nano:30b

It rendered something! Got the calendar theme, colorful meeting blocks, even a "Meetings Cleared" counter. But... where's the ball? Where's the paddle? Apparently Nemotron thought breakout meant "break out of implementing game mechanics." Nice UI though.

This one makes me a bit sad, honestly. NVIDIA has been releasing incredible AI stuff lately - the voice models alone are mind-blowing - but Nemotron-3-Nano just flopped here. Maybe it shines elsewhere, but for Claude Code workflows? Not ready.

ministral-3:14b

I really tried with this one. Multiple attempts, different prompts, fresh sessions. Every single time: Claude Code just... completed. Immediately. No files, no code, no output. It's like the model and Claude Code looked at each other and mutually agreed to do nothing. 🤷

rnj-1

"8B parameter open-weight, dense models trained from scratch by Essential AI, optimized for code and STEM with capabilities on par with SOTA open-weight models." - 130k downloads, fresh release, sounded promising. The VSCode + Cline demo on their blog looked great - but that was a Python game, not our HTML/JS breakout challenge.

Unfortunately, no results here. Another one that just didn't produce anything usable with Claude Code. At least the resource usage was... well, not surprising for an 8B model.

Summary

So, Ollama now speaks the Messages API, and Claude Code can talk to local models. I put it through a fun benchmark - building a calendar breakout game - and the results were clear: Opus still delivers the goods, but the OSS models are catching up fast.

For Claude Code agentic workflows, qwen3-coder is the clear winner in the OSS space - fast, capable, and actually follows instructions. glm-4.7-flash has potential but needs work on following prompts. gpt-oss:20b is a solid middle-ground option.

This actually reflects my broader model philosophy: ChatGPT-style models for basic tasks - rephrasing, docs, research. Anthropic for serious coding work. And now in the open-source world, qwen3-coder is genuinely fun to work with.

Is this going to replace Claude Opus for serious work? Not today. But for experimentation, learning, and those times when you want to see what the open-source world can do? This setup is fantastic.

Next Steps

Want to try this yourself?

Install Ollama ≥ 0.14: curl -L https://ollama.com/install.sh | sh
Pull your model of choice: ollama pull qwen3-coder:30b
Bump the context window to at least 32k
Add the PowerShell helper to your $PROFILE
Fire up Claude Code and start breaking some meetings... I mean, coding

And who knows - at the rate these models are improving, maybe in a few months I'll have to eat my words about frontline models being irreplaceable.

In the meantime, building a game that lets you destroy your weekly meetings with a bouncing ball might be the most satisfying thing I've prompted this month.

Happy hacking! 🎮