Code Runner MCP

The Code Runner MCP solves a common bottleneck for developers who rely on AI assistants: the need to test, validate, and experiment with code snippets in a safe, instant environment. Traditional workflows require installing packages locally or spinning up containers, which can be slow and error‑prone. This MCP server provides a single, unified endpoint that lets an AI assistant execute JavaScript/TypeScript or Python code with on‑demand package imports, all within a tightly controlled sandbox. By allowing the AI to run code directly, developers can quickly confirm logic, iterate on solutions, and reduce hallucinations—ensuring that the assistant’s suggestions are grounded in actual runtime behavior.

At its core, the server offers a secure sandbox powered by Deno for JavaScript/TypeScript and Pyodide WebAssembly for Python. Permissions are explicitly declared through environment variables, preventing accidental access to sensitive data or the host system. The sandbox isolates file‑system interactions via a mount point, giving developers fine‑grained control over which directories are exposed. This design guarantees that even if a malicious or buggy snippet is executed, it cannot compromise the underlying host.

Key capabilities include install‑on‑demand package imports from npm, JSR, and PyPI. Developers can import any library—whether a tiny utility or a heavy framework—without pre‑installing it locally. The server handles fetching, caching, and executing the code in a single request. It also supports file‑system access through configurable mount points, enabling scripts to read or write temporary files during execution. For Python users, the server leverages Pyodide’s WebAssembly runtime, ensuring that even complex numerical or data‑science workloads run efficiently in the browser or a lightweight environment.

Real‑world scenarios that benefit from this MCP include: 1) Rapid prototyping—an AI assistant can suggest a new algorithm, execute it on sample data, and return results instantly; 2) Educational tools—students can experiment with code snippets in an isolated environment without installing anything; 3) Continuous integration pipelines—CI jobs can invoke the MCP to run tests or linting scripts on demand, reducing build times; and 4) Chatbot integrations—chat platforms can let users run code snippets safely, with the assistant providing immediate feedback.

Integrating Code Runner MCP into existing AI workflows is straightforward: the server exposes a standard MCP interface, so any client that understands the protocol can send a code payload and receive stdout/stderr along with execution metadata. Because it supports both JavaScript/TypeScript and Python, developers can choose the language that best fits their stack or even combine them in a single session. The server’s design emphasizes speed, safety, and flexibility, making it a standout tool for developers who need to bridge the gap between AI suggestions and real, executable code.