Question 1

What is the MCP Server?

Accepted Answer

The MCP (Model Context Protocol) Server is a Python sandbox that allows LLMs (Large Language Models) to safely execute Python code, install packages, and generate files within isolated Docker containers. It's designed to provide a secure and controlled environment for LLMs to interact with Python code.

Question 2

How does Docker isolation enhance security?

Accepted Answer

Docker isolation ensures that all Python code is executed within isolated environments, preventing any potential security risks or conflicts with the host system. Each sandbox operates independently, providing a clean and secure slate for code execution.

Question 3

What packages can I install in the sandbox?

Accepted Answer

You can install any Python package that is available on PyPI (Python Package Index). The MCP Server provides tools to easily install and manage packages within each sandbox.

Question 4

How do I access files generated by the Python code?

Accepted Answer

The MCP Server supports file generation within the sandbox, providing web links to access these files. This allows LLMs to create and share data, reports, images, and other outputs seamlessly.

Question 5

Can I use the MCP Server with AI Agents?

Accepted Answer

Yes, the MCP Server is particularly useful for AI Agents. It provides a secure and controlled environment for agents to execute code, preventing malicious activity and ensuring the integrity of the system.

Question 6

How does the MCP Server integrate with UBOS?

Accepted Answer

The UBOS platform enhances the MCP Server's capabilities by offering a comprehensive AI Agent development environment. UBOS allows you to orchestrate AI Agents, connect them with your enterprise data, build custom AI Agents, and create Multi-Agent Systems.

Question 7

What are the available tools for interacting with the MCP Server?

Accepted Answer

The MCP Server provides several tools, including:n* `create_sandbox`: Creates a new Python Docker sandbox.n* `list_sandboxes`: Lists all existing sandboxes.n* `execute_python_code`: Executes Python code in a specified sandbox.n* `install_package_in_sandbox`: Installs Python packages in a specified sandbox.n* `check_package_installation_status`: Checks the installation status of a package.n* `execute_terminal_command`: Executes a terminal command in a sandbox.n* `upload_file_to_sandbox`: Uploads a local file to a specified sandbox.

Question 8

Where can I find example configurations?

Accepted Answer

Example configurations are provided in the documentation to demonstrate how to connect to the server, both locally and through the online demo. These configurations show how to interact with the MCP Server using the available tools.

Question 9

What should I do if a package installation is taking a long time?

Accepted Answer

You can use the `check_package_installation_status` tool to check the status of the package installation. If the package is still installing, you need to check again using this tool until the installation is complete.

Question 10

How do I handle visualizations in my Python code?

Accepted Answer

When generating visualizations, save figures to files using `plt.savefig()` instead of `plt.show()`. All saved files will automatically appear as HTTP links in the results, which you can open or embed directly.

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