Claude Code is quickly becoming a go-to AI coding assistant for developers and increasingly for non-developers who want to build with code. But to truly unlock its potential, it needs the right local infrastructure, tool access, and security boundaries.
In this blog, we’ll show you how to run Claude Code with Docker to gain full control over your models, securely connect it to real-world tools using MCP servers, and safely give it autonomy inside isolated sandboxes. Read on for practical resources to help you build a secure, private, and cost-efficient AI-powered development workflow.
Run Claude Code Locally with Docker Model Runner
This post walks through how to configure Claude Code to use Docker Model Runner, giving you full control over your data, infrastructure, and spend. Claude Code supports custom API endpoints through the ANTHROPIC_BASE_URL environment variable. Since Docker Model Runner exposes an Anthropic-compatible API, integrating the two is simple. This allows you to run models locally while maintaining the Claude Code experience.
With your model running under your control, it’s time to connect Claude Code to tools to expand its capabilities.
How to Add MCP Servers to Claude Code with Docker MCP Toolkit
MCP is becoming the de facto standard to connect coding agents like Claude Code to your real tools, databases, repositories, browsers, and APIs. With more than 300 pre-built,containerized MCP servers, one-click deployment in Docker Desktop, and automatic credential handling, developers can connect Claude Code to trusted environments in minutes — not hours. No dependency issues, no manual configuration, just a consistent, secure workflow across Mac, Windows, and Linux.
In this guide, you’ll learn how to:
- Set up Claude Code and connect it to Docker MCP Toolkit.
- Configure the Atlassian MCP server for Jira integration.
- Configure the GitHub MCP server to access repository history and run git commands.
- Configure the Filesystem MCP server to scan and read your local codebase.
- Automate tech debt tracking by converting 15 TODO comments into tracked Jira tickets.
- See how Claude Code can query git history, categorize issues, and create tickets — all without leaving your development environment.
Prefer a video walkthrough? Check out our tutorial on how to add MCP servers to Claude Code with Docker MCP Toolkit.
Connecting tools unlocks powerful automation but with greater capability comes greater responsibility. If you’re going to let agents take action, you need to run them safely.
Docker Sandboxes: Run Claude Code and Other Coding Agents Unsupervised (but Safely)
As Claude Code moves from suggestions to real-world actions like installing packages and modifying files, isolation becomes critical.
Sandboxes provide disposable, isolated environments purpose-built for coding agents. Each agent runs in an isolated version of your development environment, so when it installs packages, modifies configurations, deletes files, or runs Docker containers, your host machine remains untouched.
This isolation lets you run agents like Claude Code with autonomy. Since they can’t harm your computer, let them run free. Check out our announcement on more secure, easier to use, and more powerful Docker Sandboxes.
Summary
Claude Code is powerful on its own but when used with Docker, it becomes a secure, extensible, and fully controlled AI development environment.
In this post, you learned how to:
- Run Claude Code locally using Docker Model Runner with an Anthropic-compatible API endpoint, giving you full control over your data, infrastructure, and cost.
- Connect Claude Code to tools using the Docker MCP Toolkit, with 300+ containerized MCP servers for services like Jira, GitHub, and local filesystems — all deployable in one click.
- Run Claude Code safely in Docker Sandboxes, isolated environments that allow coding agents to operate autonomously without risking your host machine.
By combining local model execution, secure tool connectivity, and isolated runtime environments, Docker enables you to run AI coding agents like Claude Code with both autonomy and control, making them practical for real-world development workflows.