A toy MCP to let AI agents do SW-emulated Vulkan through Mesa, VkRunner, shaderc, and Docker
{
"mcpServers": {
"shaderc-vkrunner-mcp": {
"command": "<see-readme>",
"args": []
}
}
}No install config available. Check the server's README for setup instructions.
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A toy MCP to let AI agents do SW-emulated Vulkan through Mesa, VkRunner, shaderc, and Docker
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No package registry to scan.
No authentication — any process on your machine can connect.
CC0-1.0. View license →
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Last commit 244 days ago. 6 stars.
Will it work with my client?
Transport: stdio. Works with Claude Desktop, Cursor, Claude Code, and most MCP clients.
No automated test available for this server. Check the GitHub README for setup instructions.
No known vulnerabilities.
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= `shaderc-vkrunner-mcp`
:toc:
A toy Model Context Protocol (MCP) server for AI agents to safely code, run, and visualize GPU shaders.
image::shaderc-vkrunner-mcp.jpg[align="center", width="90%"]
== Overview
`shaderc-vkrunner-mcp` provides a secure sandbox for AI agents to develop, compile, optimize, and run GPU shaders using Vulkan. It's designed to work entirely locally, making it compatible with self-hosted LLMs and AI tools that support the Model Context Protocol.
*Key Features:*
* Completely local execution - no additional API dependencies
* Support for shader compilation, optimization, and visualization
* Docker-based for easy deployment and isolation
* Tested with VS Code Copilot's Agent MCP
* Safe execution environment for AI-generated shader code - runs on CPU in a mostly isolated container without risking physical GPU crashes or lost devices
== Requirements
The only requirement to use `shaderc-vkrunner-mcp` is *Docker*, besides the MCP-capable AI Agent of your choice. The MCP server application is packaged as a Docker image with all necessary dependencies included.
== Quick Start
=== Installation
Although there are pre-built images in the GitHub Container Registry, it is easy to build the image from source:
[source,bash]
----
git clone https://github.com/mehmetoguzderin/shaderc-vkrunner-mcp
cd shaderc-vkrunner-mcp
docker build -t shaderc-vkrunner-mcp -f Dockerfile .
----
=== Connecting to MCP Clients
==== VS Code Copilot
Add this to your VS Code `mcp.json`:
[source,json]
----
"shaderc-vkrunner-mcp": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"-v", "${workspaceFolder}:/work",
"shaderc-vkrunner-mcp",
"--work-dir",
"/work"
]
}
----
Then you can reference it in your Copilot Chat using `#compile_run_shaders` tag in Agent mode.
==== Using with MCP Inspector
For debugging and testing, you can use the MCP Inspector:
[source,bash]
----
npx @modelcontextprotocol/inspector docker run -i --rm shaderc-vkrunner-mcp
----
== Architecture and Design
`shaderc-vkrunner-mcp` is built with several key design principles:
1. *Safety through CPU emulation*: Uses Mesa's software Vulkan implementation to run shaders on CPU, eliminating risks of GPU crashes, paravirtualization issues, or lost devices that typically create barriers for GPU programming
2. *Cross-platform compatibility*: Uses Vulkan instead of CUDA/OpenCL for broader hardware support while running entirely in software
3. *Minimal but helpful*: Enables testing advanced features like atomics, subgroups, cooperative matrices, and raytracing without physical GPU hardware
4. *Type-safe implementation*: Leverages Rust's strong typing system
5. *Monolithic design*: Provides a unified interface for shader development, optimization, and visualization
== Advanced Features
`shaderc-vkrunner-mcp` supports a wide range of Vulkan features that can be explored by AI agents, all running safel
... [View full README on GitHub]