Code Sandbox MCP

The Code Sandbox MCP is a purpose‑built Model Context Protocol server that gives AI assistants a secure, isolated environment for executing arbitrary code. By leveraging Docker containerization, it shields the host system from potentially unsafe or untrusted payloads while still allowing full control over the execution context. This solves a core problem in AI‑driven development: how to run code safely without compromising the underlying infrastructure.

At its core, the server exposes a set of intuitive tools that map directly to common container operations. Developers can initialize a fresh sandbox with any Docker image, copy entire projects or individual files into the container, write new files on the fly, and execute shell commands—all through simple JSON requests. The ability to stream logs in real time keeps the AI assistant informed about progress and errors as they happen, enabling interactive debugging or iterative code refinement. Once work is complete, the sandbox can be stopped and removed cleanly, ensuring no residual state leaks between sessions.

Key capabilities include flexible container management, allowing teams to pick language runtimes, libraries, or custom images that match their project’s needs. The file operations suite makes it trivial to transfer code, data sets, or configuration files into the sandbox, while command execution supports multi‑step workflows such as installing dependencies before running a script. The server’s auto‑update feature guarantees that the underlying binary stays current without manual intervention, and cross‑platform support means it can run on Linux, macOS, or Windows hosts.

Typical use cases span from automated code reviews—where an AI assistant compiles and tests snippets—to educational environments that let students experiment with code in a safe sandbox. It also fits naturally into continuous integration pipelines, where an AI can generate tests or fix bugs and then run them in isolation before merging changes. Because the sandbox is fully isolated, sensitive data never leaves the host environment, making it suitable for regulated industries or private codebases.

In summary, Code Sandbox MCP provides a turnkey solution for embedding sandboxed execution into AI workflows. Its declarative API, robust container handling, and real‑time feedback loop give developers the confidence to let AI assistants generate and run code without risking system integrity or data exposure.