What dependencies does MCP Sandbox require?
Python 3.12+, the uv package manager, and Docker.
@JohanLi233
Basic information
Python sandboxes for llms
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-sandbox": {
"command": "uv",
"args": [
"venv"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
MCP Sandbox is a Python MCP server that enables users and LLMs to safely execute Python code and install packages in isolated Docker containers. It is designed for developers who need a secure, interactive Python runtime accessible via the Model Context Protocol.
Clone the repository, install dependencies using uv venv and uv sync, then start the server with uv run main.py. Configuration (host, port, PyPI mirror) can be customized in config.toml. The default SSE endpoint is http://127.0.0.1:8181/sse. For Claude Desktop, configure via supergateway pointing to the SSE URL, optionally including an API key if authentication is enabled.
Python 3.12+, the uv package manager, and Docker.
It uses Server-Sent Events (SSE) for real-time communication; the default endpoint is `http://127.0.0.1:8181/sse`.
Code runs in isolated Docker containers; generated files are stored in the `results/` directory and made accessible via direct HTTP links.
Yes, optional API key-based authentication can be enabled for multi-user environments.
Tools include `create_sandbox`, `list_sandboxes`, `execute_python_code`, `install_package_in_sandbox`, `check_package_installation_status`, `execute_terminal_command`, and `upload_file_to_sandbox`.
Quarkus LangChain4J Workshop that demonstrates both single AI service capabilities and Agentic AI orchestration
An MCP server that allows you to run TypeScript, JavaScript, and Python code in a sandbox on your local machine using the Deno® sandbox. This server provides a controlled environment for executing code with explicit permission controls.
This project demonstrates a decoupled real-time agent architecture that connects LangGraph agents to remote tools served by custom MCP (Modular Command Protocol) servers. The architecture enables a flexible and scalable multi-agent system where each tool can be hosted independent
Agentic SWMM is an automated, auditable, and memory-informed framework for reproducible stormwater modelling, integrating QGIS and EPA SWMM through the aiswmm runtime, reusable Skills, and MCP interfaces, with QA verification, provenance tracking, calibration support, and Codex,
A security scanner for your LLM agentic workflows
Comments