Introduction
Simplify Arm migration with the Docker MCP Toolkit and Arm MCP Server
Set up Docker MCP Toolkit with Arm, GitHub, and Sequential Thinking servers
Examine x86 AVX2 intrinsics in the demo application
Automate x86 to Arm migration with GitHub Copilot
Validate the Arm64 migration and test containers
Next Steps
Arm-based cloud instances are now widely available across major providers, including AWS Graviton, Azure Cobalt, and Google Cloud Axion. These platforms deliver strong performance-per-watt characteristics and, for many workloads, measurable cost savings compared to equivalent x86 instances.
For containerized applications written in portable, architecture-neutral code, migration can be straightforward: rebuild the container for linux/arm64 and redeploy.
However, many performance-sensitive applications are not architecture-neutral. They may include:
-mavx2)In these cases, rebuilding the container is not enough. The source code itself must be adapted for Arm.
When architecture-specific optimizations are present, migration may involve:
These steps are well understood, but they can require careful review across code, build scripts, and container configurations.
The Docker MCP Toolkit is a management interface in Docker Desktop that lets you discover, configure, and run containerized MCP (Model Context Protocol) servers. It connects these servers to AI coding assistants through a unified gateway.
Three MCP servers work together to support the migration workflow:
Arm MCP Server
Provides migration-focused tools:
migrate_ease_scan detects x86-specific code and compiler flagscheck_image and skopeo verify container architecture supportknowledge_base_search accesses learning resources, Arm intrinsics, and software version compatibilitymca performs microarchitectural performance analysisGitHub MCP Server
Enables Git repository operations including creating pull requests, managing branches, and committing changes.
Sequential Thinking MCP Server
Helps the AI assistant break down complex migration decisions into logical steps.
When connected to the Docker MCP Toolkit, an AI coding assistant like GitHub Copilot can coordinate a structured migration workflow:
linux/arm64 using check_image or skopeomigrate_ease_scan to identify AVX2 intrinsics, x86-specific flags, and other portability considerationsknowledge_base_search to find appropriate Arm SIMD equivalents for every x86 intrinsicThis Learning Path uses a real-world example: a matrix multiplication benchmark written in C++ with AVX2 intrinsics for x86. You’ll migrate it to Arm64 using the AI-assisted workflow described above.
The demo repository is available at github.com/JoeStech/docker-blog-arm-migration .
By the end of this Learning Path, you’ll have a working Arm64 container with NEON-optimized code and an automated pull request containing all migration changes.
You now understand:
Next, you’ll install and configure the Docker MCP Toolkit with the three required MCP servers.