MCP Code Runner

Overview

The mcp-code-runner server provides a lightweight, Docker‑based code execution engine that speaks the Model Context Protocol (MCP). It fills a common gap for developers who want to give AI assistants the ability to run arbitrary code snippets and retrieve the results in real time. By exposing a simple, well‑defined MCP interface, this server allows an AI model to request execution of code written in a variety of languages (Python, JavaScript, etc.) and receive the output directly back into the conversation. This eliminates the need for custom webhook setups or manual execution pipelines, streamlining experimentation and prototyping.

The core value proposition is the zero‑friction integration with existing AI workflows. When an assistant receives a user prompt that includes code, it can issue an MCP request to the server, which spins up a temporary Docker container, runs the code in isolation, and streams back the stdout, stderr, or any generated artifacts. Because Docker is used for execution, the environment remains reproducible and isolated from the host system, providing safety guarantees that are essential when executing untrusted code. Developers can quickly iterate on new features or debug complex logic without leaving the AI interface.

Key capabilities of mcp-code-runner include:

• MCP Compatibility: Implements the full MCP contract, enabling seamless discovery of resources and invocation patterns by any compliant client.
• Cross‑Language Execution: Supports multiple programming languages through preconfigured Docker images, allowing the same interface to run Python scripts, JavaScript modules, or other supported runtimes.
• Result Retrieval: Returns execution output in a structured format that can be parsed or displayed by the AI assistant, facilitating rich interactions such as code debugging or algorithm demonstration.
• Resource Isolation: Each execution runs in its own container, preventing side effects on the host and ensuring consistent performance across invocations.

Typical use cases span from educational tools—where a student asks an AI tutor to run code examples—to rapid prototyping, where developers test snippets directly within a conversational UI. It also supports continuous integration pipelines that trigger code runs from AI‑driven test suites, and can be embedded in interactive coding assistants that provide real‑time feedback on syntax or logic errors.

What sets this MCP server apart is its minimal footprint coupled with Docker’s robust isolation. By abstracting the complexity of container orchestration behind a simple MCP interface, it gives developers and AI practitioners a powerful, safe, and extensible mechanism to integrate code execution into any conversational or workflow‑driven application.