Script MCP – Command‑Line Execution for AI Assistants

Script MCP is a lightweight MCP server that exposes a single, well‑defined tool for running arbitrary command‑line scripts from an AI assistant. It addresses the common developer pain point of needing to trigger shell commands, run build scripts or execute small automation tasks while keeping the execution sandboxed and secure. By packaging script execution behind an MCP interface, developers can integrate native shell workflows into conversational agents without exposing the underlying host system to unrestricted command execution.

The server accepts a JSON payload that specifies the script text, interpreter (e.g., , , ), and optional arguments. It then spawns a child process, captures both standard output and error streams, and returns the results in a structured MCP response. Built‑in timeout protection guarantees that runaway scripts are terminated after a configurable period, preventing denial‑of‑service scenarios. The tool is intentionally simple yet robust: it supports cross‑platform binaries for Linux, macOS and Windows, ensuring that a single binary can run in diverse environments.

Key capabilities include:

• Safe execution: Scripts are isolated from the parent process, with no elevated privileges unless explicitly granted.
• Interpreter flexibility: The server can delegate to any installed interpreter, allowing users to run shell scripts, Python snippets, PowerShell commands, and more.
• Timeouts: A configurable timeout stops long‑running processes automatically.
• Output handling: Both stdout and stderr are captured and returned, making debugging straightforward.
• Cross‑platform support: The same MCP interface works on all major operating systems, simplifying deployment in heterogeneous infrastructure.

Typical use cases span from continuous‑integration pipelines to interactive debugging. For example, a Claude assistant can be asked to “run the unit tests for this repository” and receive real‑time test output. In a deployment workflow, an AI can trigger or commands and report success or failure back to the user. The tool also shines in educational settings, where students can experiment with shell commands through a conversational interface without risking system stability.

Integration into AI workflows is seamless: once the MCP server is registered in a client’s configuration (as shown in the README), any tool‑enabled assistant can invoke the tool by name, passing the necessary parameters. The assistant’s prompt templates can then embed dynamic command results, enabling richer, context‑aware conversations. Because the server is a standalone binary, it can be deployed in containerized environments or as part of a larger automation stack, providing developers with a secure, predictable bridge between AI agents and the underlying operating system.