MCP Script Runner

The MCP Script Runner is a lightweight server that bridges the gap between an AI assistant and arbitrary shell scripts or command‑line tools. It exposes a set of tools defined as executable files, allowing the assistant to invoke them with arguments derived from user prompts. This solves a common pain point for developers: how to safely and flexibly let an AI trigger local scripts without embedding complex logic in the assistant itself.

At its core, the server runs a single Python process that listens for MCP requests. When an assistant issues a tool call, the server locates the corresponding script in its directory and executes it using a configurable command runner (e.g., ). The output of the script is captured and returned to the assistant, which can then incorporate it into a response or feed it to subsequent tools. Because scripts are stored as plain files, developers can add, modify, or remove capabilities simply by editing the filesystem—no redeployment is required beyond a reload.

Key features include:

• Dynamic tool discovery – Any executable script placed in the folder becomes available as a tool. The server automatically detects new files after a reload.
• Configurable execution context – Environment variables and working directories can be specified per tool, enabling scripts to run in isolated or pre‑configured environments.
• Safety precautions – The README warns that scripts are executed directly, so developers must ensure proper shebang lines and file permissions. The server is intended for local use only, preventing accidental exposure of sensitive commands.
• Lightweight integration – By leveraging the standard MCP interface, the script runner plugs into any AI workflow that supports MCP without requiring custom adapters.

Typical use cases include:

• Automated build or deployment pipelines – An assistant can trigger , , or other CI scripts in response to natural language commands.
• Data processing workflows – Scripts that scrape, transform, or analyze data can be invoked on demand, with results fed back into the conversation.
• System administration – Routine maintenance tasks such as backups, log rotation, or service restarts can be controlled through the AI interface.
• Rapid prototyping – Developers can expose new command‑line tools to an assistant by simply adding a script, enabling quick experimentation and iteration.

By abstracting the execution of arbitrary scripts into an MCP server, developers gain a flexible, secure, and developer‑friendly mechanism to extend AI assistants. The model context protocol ensures that the assistant remains stateless while still orchestrating complex local operations, making the MCP Script Runner a valuable addition to any AI‑augmented development workflow.