How to build an AI-powered code quality workflow with SonarQube and E2B

This guide demonstrates how to build an AI-powered code quality workflow using E2B sandboxes with Docker’s MCP catalog. You’ll create a system that automatically analyzes code quality issues in GitHub repositories using SonarQube, then generate pull requests with fixes.

What you'll build

You’ll build a Node.js script that spins up an E2B sandbox, connects GitHub and SonarQube MCP servers, and uses Claude Code to analyze code quality and propose improvements. The MCP servers are containerized and run as part of the E2B sandbox.

What you'll learn

In this guide, you'll learn:

• How to create E2B sandboxes with multiple MCP servers
• How to configure GitHub and SonarQube MCP servers for AI workflows
• How to use Claude Code inside sandboxes to interact with external tools
• How to build automated code review workflows that create quality-gated pull requests

Why use E2B sandboxes?

Running this workflow in E2B sandboes provides several advantages over local execution:

• Security: AI-generated code runs in isolated containers, protecting your local environment and credentials
• Zero setup: No need to install SonarQube, GitHub CLI, or manage dependencies locally
• Scalability: Resource-intensive operations like code scanning run in the cloud without consuming local resources

Learn more

Read Docker's blog post: Docker + E2B: Building the Future of Trusted AI.