milkymap/mcp4modal_sandbox

MCP4Modal Sandbox

A powerful Model Context Protocol (MCP) server that provides seamless cloud-based sandbox management using Modal.com. This project enables LLMs and AI assistants to spawn, manage, and interact with isolated compute environments in the cloud with full GPU support.

Features

Core Sandbox Management

• Launch Sandboxes: Create isolated Python environments with custom configurations
• Terminate Sandboxes: Clean resource management and controlled shutdown
• List Sandboxes: Monitor and track active sandbox environments
• App Namespacing: Organize sandboxes within Modal app namespaces

Advanced Configuration

• Python Versions: Support for multiple Python versions (default: 3.12)
• Package Management: Install pip and apt packages during sandbox creation
• Resource Allocation: Configure CPU cores, memory, and execution timeouts
• Working Directory: Set custom working directories for sandbox environments

GPU Support

Comprehensive GPU support for machine learning and compute-intensive workloads:

• T4: Entry-level GPU, ideal for inference workloads
• L4: Mid-range GPU for general ML tasks
• A10G: High-performance GPU for training (up to 4 GPUs)
• A100-40GB/80GB: High-end GPUs for large-scale training
• L40S: Latest generation GPU for ML workloads
• H100: Latest generation high-end GPU
• H200: Latest generation flagship GPU
• B200: Latest generation enterprise GPU

File Operations

• Push Files: Upload files from local filesystem to sandboxes
• Pull Files: Download files from sandboxes to local filesystem
• Read File Content: View file contents directly without downloading
• Write File Content: Create and edit files within sandboxes
• Directory Management: Create, list, and remove directories

Command Execution

• Remote Execution: Run arbitrary commands in sandbox environments
• Output Capture: Capture stdout, stderr, and return codes
• Timeout Control: Configure execution timeouts for long-running tasks
• Performance Metrics: Track execution time and resource usage

Security & Environment Management

• Secrets Management: Inject environment variables and secrets
• Predefined Secrets: Reference existing secrets from Modal dashboard
• Volume Mounting: Attach persistent storage volumes
• Isolated Environments: Complete isolation between sandbox instances

Transport Options

• stdio: Direct command-line interface (default)
• streamable-http: HTTP-based communication
• SSE: Server-Sent Events for real-time updates

rerequisites

• Python 3.12+
• Modal.com account and API key
• Environment variables configured (see Configuration section)

Installation

Using UV (Recommended)

# Clone the repository
git clone https://github.com/milkymap/mcp4modal_sandbox.git
cd mcp4modal_sandbox

# Install dependencies
uv sync

# Install in development mode
uv pip install -e .

Using Docker

Build the Docker Image

# Build the Docker image
docker build -t mcp4modal-sandbox f Dockerfile .

Run with stdio Transport (Default)

# Run --help to find options
docker run -it \
  -e MODAL_TOKEN_ID="your_modal_token_id" \
  -e MODAL_TOKEN_SECRET="your_modal_token_secret" \
  mcp4modal-sandbox --help

Configuration

Environment Variables

Create a .env file in the project root:

# Required: Modal.com API Configuration
MODAL_TOKEN_ID="your_modal_token_id"
MODAL_TOKEN_SECRET="your_modal_token_secret"

# Optional: HTTP Transport Configuration (only needed for streamable-http/sse transports)
MCP_HOST="0.0.0.0"  # Default: 0.0.0.0
MCP_PORT=8000       # Default: 8000

Modal.com Setup

1. Create an account at Modal.com
2. Generate API tokens from your Modal dashboard
3. Configure the tokens in your environment variables

Integration with Claude Desktop

Add to your Claude Desktop configuration:

uvx

{
  "mcpServers": {
    "mcp4modal-sandbox": {
        "command": "uvx",
        "args": [
          "mcp4modal-sandbox",
          "--transport", "stdio",
          "--app_name", "namespace",
          "--preloaded_secrets", "group0", // modal secret
          "--preloaded_secrets", "group1" // modal secret
          ],
        "env": {
          "MODAL_TOKEN_ID": "",
          "MODAL_TOKEN_SECRET": ""
        }
    }
  }
}

docker

{
  "mcpServers": {
    "mcp4modal-sandbox": {
        "command": "docker",
        "args": [
          "run", "--rm", "-i", "--name", "modal_sandbox",
          "-e", "MODAL_TOKEN_ID", "-e", "MODAL_TOKEN_SECRET", 
          "-v", "/path/to/volume", 
          "milkymap/modal_sandbox:0.1", 
          "--transport", "stdio",
          "--app_name", "namespace",
          "--preloaded_secrets", "group0",
          "--preloaded_secrets", "group1"
          ],
        "env": {
          "MODAL_TOKEN_ID": "",
          "MODAL_TOKEN_SECRET": ""
        }
    }
  }
}

Available Tools

The MCP server provides 11 tools for comprehensive sandbox management:

1. launch_sandbox - Create new Modal sandboxes with custom configuration (Python version, packages, GPU, secrets)
2. terminate_sandbox - Stop and clean up running sandboxes
3. list_sandboxes - List all sandboxes in an app namespace with their status
4. execute_command - Run shell commands in sandboxes and capture output
5. push_file_to_sandbox - Upload files from local filesystem to sandboxes
6. pull_file_from_sandbox - Download files from sandboxes to local filesystem
7. list_directory_contents - List contents of directories within sandboxes
8. make_directory - Create directories in sandboxes
9. remove_path - Remove files or directories from sandboxes
10. read_file_content_from_sandbox - Read file contents directly from sandboxes
11. write_file_content_to_sandbox - Write content to files within sandboxes