MCP Code Executor: Secure Python Execution in Isolated Environments

MCP Code Executor empowers LLMs to run Python code securely within isolated Conda environments, bridging AI logic with real-world execution seamlessly.

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About MCP Code Executor

What is MCP Code Executor: Secure Python Execution in Isolated Environments?

Picture this: you're an AI developer juggling multiple projects, each demanding different Python dependencies. Enter the MCP Code Executor—a nifty tool that lets Large Language Models (LLMs) execute Python code in pristine Conda environments. Think of it as a digital sandbox where code runs without touching your base system. It’s like having a Swiss Army knife for reproducible coding, ensuring that every script operates in its own sterile, dependency-safe bubble. Perfect for when you need to test that experimental library without contaminating your main workspace.

How to Use MCP Code Executor: Secure Python Execution in Isolated Environments?

Let’s say you want to run a TensorFlow script but don’t want it cluttering your default environment. First, clone the repo and install Node.js dependencies. Then, configure your Conda environment name (e.g., "tensorflow-venv") and a storage directory—maybe a cloud-connected drive for easy access. Fire up the server, and your LLMs can now generate code snippets that automatically execute in that isolated space. Imagine telling your AI assistant: “Run this ML model in the PyTorch environment,” and watching it happen without lifting a finger.

MCP Code Executor Features

Key Features of MCP Code Executor: Secure Python Execution in Isolated Environments?

This tool’s secret sauce is its trio of safeguards: environment pinning (no stray dependencies), file-based persistence (code snippets live on after execution), and path locking (no accidental overwrites). The storage directory acts like a digital filing cabinet—configure it once, and every experiment stays neatly organized. We especially love the “fail-safe” logging that shows exactly which packages were loaded during runtime, making debugging feel like solving a puzzle instead of a guessing game.

Use Cases of MCP Code Executor: Secure Python Execution in Isolated Environments?

MCP Code Executor FAQ

FAQ from MCP Code Executor: Secure Python Execution in Isolated Environments?

Is it really secure? Like a Fort Knox for code—executions run as non-root processes with strict path restrictions. Does it support GPU environments? Absolutely! Just install CUDA in your Conda env. Can I share environments between projects? Yep—just symlink your YAML files. And for the curious: Why Node.js? It’s the fastest way to bridge LLM APIs with system processes, according to our benchmarks (we’re biased, but it’s true).

Content

MCP Code Executor

The MCP Code Executor is an MCP server that allows LLMs to execute Python code within a specified Conda environment. This enables LLMs to run code with access to libraries and dependencies defined in the Conda environment.

Features

Execute Python code from LLM prompts
Run code within a specified Conda environment
Configurable code storage directory

Prerequisites

Node.js installed
Conda installed
Desired Conda environment created

Setup

Clone this repository:

git clone https://github.com/bazinga012/mcp_code_executor.git

Navigate to the project directory:

cd mcp_code_executor

Install the Node.js dependencies:

npm install

Build the project:

npm run build

Configuration

To configure the MCP Code Executor server, add the following to your MCP servers configuration file:

{
  "mcpServers": {
    "mcp-code-executor": {
      "command": "node",
      "args": [
        "/path/to/mcp_code_executor/build/index.js" 
      ],
      "env": {
        "CODE_STORAGE_DIR": "/path/to/code/storage",
        "CONDA_ENV_NAME": "your-conda-env"
      }
    }
  }
}

Replace the placeholders:

/path/to/mcp_code_executor with the absolute path to where you cloned this repository
/path/to/code/storage with the directory where you want the generated code to be stored
your-conda-env with the name of the Conda environment you want the code to run in

Usage

Once configured, the MCP Code Executor will allow LLMs to execute Python code by generating a file in the specified CODE_STORAGE_DIR and running it within the Conda environment defined by CONDA_ENV_NAME.

LLMs can generate and execute code by referencing this MCP server in their prompts.

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

License

This project is licensed under the MIT License.