# 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](https://e2b.dev/docs) 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](https://www.docker.com/blog/docker-e2b-building-the-future-of-trusted-ai/).