https://github.com/tsuchijo/sandbox-mcp
https://github.com/tsuchijo/sandbox-mcp
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/tsuchijo/sandbox-mcp
- Owner: Tsuchijo
- Created: 2025-01-11T00:00:34.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-01-11T00:00:42.000Z (9 months ago)
- Last Synced: 2025-06-17T13:20:25.789Z (4 months ago)
- Language: Python
- Size: 17.6 KB
- Stars: 9
- Watchers: 1
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# Sandbox MCP Server
An MCP server that provides isolated Docker environments for code execution. This server allows you to:
- Create containers with any Docker image
- Write and execute code in multiple programming languages
- Install packages and set up development environments
- Run commands in isolated containers
## Prerequisites
- Python 3.9 or higher
- Docker installed and running
- uv package manager (recommended)
- Docker MCP server (recommended)
## Installation
1. Clone this repository:
```bash
git clone
cd sandbox_server
```
2. Create and activate a virtual environment with uv:
```bash
uv venv
source .venv/bin/activate # On Unix/MacOS
# Or on Windows:
# .venv\Scripts\activate
```
3. Install dependencies:
```bash
uv pip install .
```
## Integration with Claude Desktop
1. Open Claude Desktop's configuration file:
- macOS: `~/Library/Application Support/Claude/claude_desktop_config.json`
- Windows: `%APPDATA%\Claude\claude_desktop_config.json`
2. Add the sandbox server configuration:
```json
{
"mcpServers": {
"sandbox": {
"command": "uv",
"args": [
"--directory",
"/absolute/path/to/sandbox_server",
"run",
"sandbox_server.py"
],
"env": {
"PYTHONPATH": "/absolute/path/to/sandbox_server"
}
}
}
}
```
Replace `/absolute/path/to/sandbox_server` with the actual path to your project directory.
3. Restart Claude Desktop
## Usage Examples
### Basic Usage
Once connected to Claude Desktop, you can:
1. Create a Python container:
```
Could you create a Python container and write a simple hello world program?
```
2. Run code in different languages:
```
Could you create a C program that calculates the fibonacci sequence and run it?
```
3. Install packages and use them:
```
Could you create a Python script that uses numpy to generate and plot some random data?
```
### Saving and Reproducing Environments
The server provides several ways to save and reproduce your development environments:
#### Creating Persistent Containers
When creating a container, you can make it persistent:
```
Could you create a persistent Python container with numpy and pandas installed?
```
This will create a container that:
- Stays running after Claude Desktop closes
- Can be accessed directly through Docker
- Preserves all installed packages and files
The server will provide instructions for:
- Accessing the container directly (`docker exec`)
- Stopping and starting the container
- Removing it when no longer needed
#### Saving Container State
After setting up your environment, you can save it as a Docker image:
```
Could you save the current container state as an image named 'my-ds-env:v1'?
```
This will:
1. Create a new Docker image with all your:
- Installed packages
- Created files
- Configuration changes
2. Provide instructions for reusing the environment
You can then share this image or use it as a starting point for new containers:
```
Could you create a new container using the my-ds-env:v1 image?
```
#### Generating Dockerfiles
To make your environment fully reproducible, you can generate a Dockerfile:
```
Could you export a Dockerfile that recreates this environment?
```
The generated Dockerfile will include:
- Base image specification
- Created files
- Template for additional setup steps
You can use this Dockerfile to:
1. Share your environment setup with others
2. Version control your development environment
3. Modify and customize the build process
4. Deploy to different systems
#### Recommended Workflow
For reproducible development environments:
1. Create a persistent container:
```
Create a persistent Python container for data science work
```
2. Install needed packages and set up the environment:
```
Install numpy, pandas, and scikit-learn in the container
```
3. Test your setup:
```
Create and run a test script to verify the environment
```
4. Save the state:
```
Save this container as 'ds-workspace:v1'
```
5. Export a Dockerfile:
```
Generate a Dockerfile for this environment
```
This gives you multiple options for recreating your environment:
- Use the saved Docker image directly
- Build from the Dockerfile with modifications
- Access the original container if needed
## Security Notes
- All code executes in isolated Docker containers
- Containers are automatically removed after use
- File systems are isolated between containers
- Host system access is restricted
## Project Structure
```
sandbox_server/
├── sandbox_server.py # Main server implementation
├── pyproject.toml # Project configuration
└── README.md # This file
```
## Available Tools
The server provides three main tools:
1. `create_container_environment`: Creates a new Docker container with specified image
2. `create_file_in_container`: Creates a file in a container
3. `execute_command_in_container`: Runs commands in a container
4. `save_container_state`: Saves the container state to a persistent container
5. `export_dockerfile`: exports a docker file to create a persistant environment
6. `exit_container`: closes a container to cleanup environment when finished