Python Sandbox
P

Python Sandbox

Python MCP Sandbox is an interactive code execution tool that can securely run Python code and manage packages in isolated Docker containers
2.5 points
7.9K

What is Python MCP Sandbox?

Python MCP Sandbox is a secure code execution environment that allows users and AI models to run Python code and install third - party packages in isolated Docker containers. It provides complete isolation of the Python environment, ensuring that code execution does not affect the host system.

How to use Python MCP Sandbox?

Through a simple API interface or SSE connection, you can create isolated environments, run code, install packages, and manage generated files. All operations are identified by the sandbox_id to distinguish different isolated environments.

Applicable scenarios

Suitable for scenarios that require secure execution of untrusted code, such as AI code generation testing, teaching demonstrations, automated data processing, etc. It is particularly suitable for use with LLMs to execute generated code.

Main features

Docker isolation
All code is executed in fully isolated Docker containers to ensure the security of the host system
Package management
Supports installing and managing Python third - party packages and automatically handles dependencies
File generation
Generated images, data files, etc. can be accessed via direct links
Terminal access
Supports executing terminal commands within the sandbox
File upload
Allows uploading local files to the sandbox environment for use
Advantages
Fully isolated execution environment to ensure system security
Flexible package management, supporting mainstream Python packages
Generated files can be directly accessed via URL
Supports seamless integration with AI models
Limitations
Requires Docker environment support
Execution resources are limited by the container
Network access may be restricted

How to use

Installation preparation
Ensure that Docker and Python 3.12+ are installed
Clone and start the service
Get the code repository and start the service
Connect to the MCP client
Configure the MCP client to connect to the SSE endpoint

Usage examples

Data analysis
Use pandas for data analysis and generate charts
Machine learning
Train a simple machine learning model

Frequently Asked Questions

How to view the created sandboxes?
Why is there no output when my code is executed?
How to access the generated files?

Related resources

Online demonstration
An online demonstration environment that can be directly experienced
GitHub repository
Project source code and documentation
MCP protocol
MCP protocol specification documentation

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "mcpSandbox": {
      "command": "npx",
      "args": ["-y", "supergateway", "--sse",  "http://localhost:8000/sse"]
    }
  }
}

{
  "mcpServers": {
    "mcpSandbox": {
      "command": "npx",
      "args": ["-y", "supergateway", "--sse",  "http://115.190.87.78/sse?api_key=<API_KEY>"]
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.

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