# [Automate x86-to-Arm application migration using Arm MCP Server](https://learn.arm.com/learning-paths/servers-and-cloud-computing/arm-mcp-server/)

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/servers-and-cloud-computing/arm-mcp-server/)
- [Understand the Arm MCP Server for AI-driven x86-to-Arm migration](https://learn.arm.com/learning-paths/servers-and-cloud-computing/arm-mcp-server/1-overview/)
- [Verify Docker image compatibility with Arm using AI](https://learn.arm.com/learning-paths/servers-and-cloud-computing/arm-mcp-server/2-docker-check/)
- [Arm Cloud Migration Agent in GitHub Copilot](https://learn.arm.com/learning-paths/servers-and-cloud-computing/arm-mcp-server/3-simd-migration/)
- [Configure other AI agents to automate Arm migration workflows](https://learn.arm.com/learning-paths/servers-and-cloud-computing/arm-mcp-server/4-agentic-systems/)
- [Next Steps](https://learn.arm.com/learning-paths/servers-and-cloud-computing/arm-mcp-server/_next-steps/)

## About this Learning Path

| Skill level:        | Advanced          |
|---------------------|-------------------|
| Reading time:       | 20 min            |
| Last updated:       | 29 Jun 2026       |

| Author:                                   |
|-------------------------------------------|
| Joe Stech, Arm [GitHub](https://github.com/JoeStech) [LinkedIn](https://linkedin.com/in/joestech) |

| Arm IP:                                   |
|-------------------------------------------|
| [Neoverse](https://support.arm.com/?tab=compute-ip&Product%20Type=Infrastructure%20Processors) |

| Tags:                                     |
|-------------------------------------------|
| [Performance and Architecture](https://learn.arm.com/tag/performance-and-architecture) [Linux](https://learn.arm.com/tag/linux) [MCP](https://learn.arm.com/tag/mcp) [Docker](https://learn.arm.com/tag/docker) [CPP](https://learn.arm.com/tag/cpp) [GitHub Copilot](https://learn.arm.com/tag/github-copilot) |

### Who is this for?
This is an advanced topic for developers who want to use AI-powered tools to migrate x86 applications to Arm-based cloud instances.

### What will you learn?
Upon completion of this Learning Path, you will be able to:
- Explain how the Arm MCP Server enables AI-driven x86-to-Arm migration workflows
- Use AI-assisted checks to inspect Docker images for Arm compatibility
- Set up and use the Arm Cloud Migration Agent in GitHub Copilot to automate x86-to-Arm code migration
- Validate and run a migrated C++ application using Docker on Arm-based systems
- Configure other AI agents to reuse the same migration workflow across different tools

### Prerequisites
Before starting, you will need the following:
- An AI-powered IDE such as VS Code, Copilot in VS Code, Kiro (IDE or CLI) or Codex
- Basic familiarity with Docker and C/C++ development
- Access to an Arm-based cloud instance or local Arm computer running Linux or macOS

### Summary
You’ll connect an AI coding assistant to the Arm MCP Server and use its Model Context Protocol capabilities to drive an x86-to-Arm migration workflow. First, you’ll configure the Arm Cloud Migration Agent in GitHub Copilot to identify and refactor SIMD intrinsics from SSE/AVX to Arm Neon or SVE. Then, you’ll review AI-generated changes and validate outcomes by building and running the migrated C++ application in Docker on Arm-based systems. You’ll also learn how to replicate the same multi-step workflow in other agentic tools using persistent, structured instructions.

### Frequently asked questions
<details>
<summary>How do I know if a Docker base image is compatible with Arm?</summary>
Ask the Arm MCP Server in natural language to check the image’s supported architectures. Expect a response indicating whether `arm64` is included; if it is missing, select an Arm-compatible image before proceeding.
</details>

<details>
<summary>What confirms that my AI assistant is connected to the Arm MCP Server?</summary>
Prompts about Arm migration tasks return structured, tool-backed results instead of generic text. For example, image compatibility queries or code analysis requests yield specific findings sourced through MCP.
</details>

<details>
<summary>How should I handle x86 SIMD intrinsics during migration?</summary>
Use the Arm Cloud Migration Agent to locate SSE/AVX usage and propose Neon or SVE equivalents. Review the suggested refactoring, update the code, and compile to verify that the changes build cleanly.
</details>

<details>
<summary>What should I check before running the migrated app on an Arm system?</summary>
Confirm the container base image supports `arm64` and that dependencies are available for Arm. Rebuild the container and ensure the application starts without x86-specific instruction errors.
</details>

<details>
<summary>How can I reuse this workflow in other AI tools?</summary>
Create persistent instructions (such as steering documents or prompt files) that direct the agent to use the Arm MCP Server and follow the same migration steps. Mirror the Copilot setup so the agent can perform checks, refactoring, and validation consistently.
</details>