jupyter-interpreter-mcp

# Jupyter Interpreter MCP A remote Jupyter-based MCP (Model Context Protocol) server for code interpretation. This server connects to a remote Jupyter server (e.g. running in a Docker container or cloud instance) and provides a persistent, sandboxed code execution environment similar to Jupyter notebooks. Supports both Python and bash command execution. ## Architecture ``` MCP Server → RemoteJupyterClient → Jupyter REST API → Remote Kernel ↓ WebSocket Connection ↓ Jupyter server Filesystem ``` All code executes within the remote Jupyter server. Session history files are stored in the server's filesystem, not on the host. You can execute both Python code and bash commands (e.g., ls, pwd, cat file.txt). Requirements ## Requirements - Python 3.10 or higher - uv package manager - Network access to a Jupyter server ## Quick Start ### 1. (Optional) Start Jupyter Container This is only necessary if you don't use any other remote instance of Jupyter. Run a Jupyter container with the required port mappings, e.g.: ```bash docker run -d \ --name jupyter-notebook \ -p 8889:8888 \ jupyter/minimal-notebook:latest ``` ### 2. Get Authentication Token Create a new token for accessing the Jupyter server or use an existing token. ### 3. Run the MCP server #### Using uvx Start the server using uvx: ```bash uvx jupyter-interpreter-mcp --jupyter-base-url http://localhost:8889 --jupyter-token abc123def456... --sessions-dir /home/jovyan/sessions --session-ttl 3600 ``` or to add it to e.g. Claude Code: ```json { "mcpServers": { "jupyter-interpreter-mcp": { "command": "uvx", "args": [ "jupyter-interpreter-mcp", "--jupyter-base-url", "http://localhost:8889", "--jupyter-token", "abc123def456...", "--sessions-dir", "/home/jovyan/sessions", "--session-ttl…