[Node.js Sandbox MCP Server] Model Context Protocol (MCP) Server

Overview: What is Node.js Sandbox MCP Server?

The Node.js Sandbox MCP Server is designed to facilitate the execution of arbitrary JavaScript in ephemeral Docker containers, providing a sandbox environment for developing and testing scripts on the fly. This server leverages Model Context Protocol (MCP), a standardized interface enabling seamless integration with various AI applications like Claude Desktop, Continue, and Cursor.

🔧 Core Features & MCP Capabilities

The Node.js Sandbox MCP Server offers robust features that enhance its utility in AI workflows:

• Isolated Docker Containers: Each job runs within a fresh, isolated container, ensuring no state is carried over between executions.
• Dynamic NPM Dependency Management: You can specify and install npm dependencies as needed for each script, making it easy to support a wide range of functionality without cluttering the global environment.
• Customizable Environment Setup: The server supports both session-based containers and one-shot instances, allowing developers to choose the workflow that best fits their needs.

⚙️ MCP Architecture & Protocol Implementation

The Node.js Sandbox MCP Server implements Model Context Protocol (MCP) to ensure compatibility with various AI applications. The protocol itself is designed to standardize interactions between clients and servers, fostering a user-friendly experience across different platforms and tools.

MCP Server Workflow

1. Start a New Sandbox: Use the sandbox_initialize API call to launch a sandbox container.
2. Execute Commands: Utilize the sandbox_exec API for running shell commands in the active sandbox context.
3. Run JavaScript: The run_js_ephemeral and run_js APIs allow you to run JS snippets or full scripts with the option of pre-install npm dependencies.
4. Clean Up: Terminate the container using the sandbox_stop API to ensure resources are freed up.

Mermaid Diagram: MCP Protocol Flow

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

Mermaid Diagram: Data Architecture

graph TD;
    subgraph AI Application
        A[Client] -->|Request| B[MCP Client]
    end
    subgraph MCP Protocol
        B --> C[Node.js Sandbox Server]
        C --> D[Container Management]
        D --> E[Data Source/Tool]
        F[Captured Output]
    end

🚀 Getting Started with Installation

To get started, follow these steps:

1. Clone the Repository: Obtain the Node.js Sandbox MCP server code from GitHub.

   git clone https://github.com/user/node-sandbox-mcp.git
   

2. Navigate to the Code Directory:

   cd node-sandbox-mcp
   

3. Install Dependencies: Ensure npm is installed and then run the following command:

   npm install
   

4. Build the Server:

   npm run build
   

5. Run the MCP Server: You can choose to run it in a Docker container if needed.

   docker run --rm -it \
     -v /var/run/docker.sock:/var/run/docker.sock \
     alfonsograziano/node-code-sandbox-mcp stdio
   

💡 Key Use Cases in AI Workflows

Case Study 1: Automated Testing with Node.js Sandboxes

Developers can create a set of test cases for a new library or module. Each test is run within an isolated sandbox, ensuring consistency and preventing interference between different tests.

// Example Test Script
const fs = require('fs/promises');

await fs.writeFile("test-result.txt", "Test completed successfully!");
console.log("Saved test-result.txt");

Case Study 2: Dynamic Data Analysis with Varying Dependencies

Perform real-time data analysis on datasets where the necessary tools and libraries might change frequently. The server allows for easy installation and removal of dependencies as needed.

🔌 Integration with MCP Clients

The Node.js Sandbox MCP Server supports integration with popular AI applications:

• Claude Desktop: Compatible, enabling seamless execution of scripts.
• Continue: Fully supported, ensuring reliable performance.
• Cursor: Currently only supports tool-level integration but not prompt functionality or API key management.

Mermaid Diagram: MCP Client Compatibility Matrix

graph TD;
    subgraph "MCP Client Compatibility"
        A[Claude Desktop] -->|Supports| B[AI Application Integration]
        C[Continue] -->|Full Support| D[Data Source/Tool Access]
        E[Cursor] -->|Tools Only| F[Prompt Processing Limitations]
    end

📊 Performance & Compatibility Matrix

The server has been tested for compatibility with several platforms and tools:

• Linux: Fully tested with high performance.
• Windows: Supported, but may require additional setup steps.
• macOS: Known to work, however limited testing was done.

🛠️ Advanced Configuration & Security

Environmental Variables

Configure environmental variables as needed for the server instance:

{
  "mcpServers": {
    "js-sandbox": {
      "command": "node",
      "args": ["dist/server.js", "stdio"],
      "cwd": "/path/to/js-sandbox-mcp",
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

Security Measures

• Docker Best Practices: Ensure that only the necessary permissions are granted when mounting Docker sockets or volumes.
• Rate Limiting: Implement rate limiting to prevent abuse and ensure fair usage.

❓ Frequently Asked Questions (FAQ)

1. Does this server support all MCP clients?
   • Currently, Claude Desktop has full compatibility with Node.js Sandbox. Continue and Cursor have partial integration, focusing on tools rather than AI prompts.
2. Is it possible to run multiple scripts in one container?
   • Yes, the sandbox_initialize method starts a persistent sandbox that can be re-used for running multiple commands or scripts across sessions.
3. How do I handle environment-specific dependencies?
   • Use the dependencies argument in the run_js_ephemeral and run_js APIs to specify which npm packages are required by each script.
4. Can I use this server on Windows as well?
   • Yes, it is supported but may need additional configuration to ensure compatibility with Docker on Windows.
5. What happens if the container memory limit is exceeded?
   • If a sandboxed execution hits the CPU or memory limits set by the host system, it will be automatically terminated and cleaned up.

👨‍💻 Development & Contribution Guidelines

• Contributing: Contributions are welcome! Feel free to submit issues or pull requests on GitHub.
• Branching Strategy: Use Git branches for features or bug fixes. Test your changes thoroughly before submitting them.
• Code of Conduct: Adhere to the project’s code of conduct, ensuring a positive contribution experience.

🌐 MCP Ecosystem & Resources

• Official Model Context Protocol (MCP) Documentation: Explore the official documentation to learn more about the protocol and how it can be leveraged in your projects.
• Community Support Channels: Engage with other developers through GitHub issues or community forums for help and advice.

Example MCP Configuration

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

By harnessing the power of Node.js Sandbox MCP Server, you can significantly enhance your AI application's capabilities, providing a robust and flexible environment for dynamic script execution and tool integration.