Jupyter MCP Server

<!-- ~ Copyright (c) 2023-2024 Datalayer, Inc. ~ ~ BSD 3-Clause License --> [](https://datalayer.io) [](https://github.com/sponsors/datalayer) # 🪐✨ Jupyter MCP Server [](https://pypi.org/project/jupyter-mcp-server) <a href="https://mseep.ai/app/datalayer-jupyter-mcp-server"> <img src="https://mseep.net/pr/datalayer-jupyter-mcp-server-badge.png" alt="MseeP.ai Security Assessment Badge" width="100" /> </a> [](https://archestra.ai/mcp-catalog/datalayer__jupyter-mcp-server) > 🚨 **NEW IN 0.14.0:** Multi-notebook support! You can now seamlessly switch between multiple notebooks in a single session. [Read more in the release notes.](https://jupyter-mcp-server.datalayer.tech/releases) **Jupyter MCP Server** is a [Model Context Protocol](https://modelcontextprotocol.io) (MCP) server implementation that enables **real-time** interaction with 📓 Jupyter Notebooks, allowing AI to edit, document and execute code for data analysis, visualization etc. Compatible with any Jupyter deployment (local, JupyterHub, ...) and with [Datalayer](https://datalayer.ai/) hosted Notebooks. ## 🚀 Key Features - ⚡ **Real-time control:** Instantly view notebook changes as they happen. - 🔁 **Smart execution:** Automatically adjusts when a cell run fails thanks to cell output feedback. - 🧠 **Context-aware:** Understands the entire notebook context for more relevant interactions. - 📊 **Multimodal support:** Support different output types, including images, plots, and text. - 📁 **Multi-notebook support:** Seamlessly switch between multiple notebooks. - 🤝 **MCP-compatible:** Works with any MCP client, such as Claude Desktop, Cursor, Windsurf, and more.  🛠️ This MCP offers multiple tools such as `insert_cell`, `execute_cell`, `list_files`, `read_cell`, and more, enabling advanced interactions with Jupyter notebooks. Explore our [tools documentation](https://jupyter-mcp-server.datalayer.tech/tools) to learn about all the tools powering Jupyter MCP Server. ## 🏁 Getting Started For comprehensive setup instructions—including `Streamable HTTP` transport and advanced configuration—check out [our documentation](https://jupyter-mcp-server.datalayer.tech/). Or, get started quickly with `JupyterLab` and `stdio` transport here below. ### 1. Set Up Your Environment ```bash pip install jupyterlab==4.4.1 jupyter-collaboration==4.0.2 ipykernel pip uninstall -y pycrdt datalayer_pycrdt pip install datalayer_pycrdt==0.12.17 ``` ### 2. Start JupyterLab ```bash # make jupyterlab jupyter lab --port 8888 --IdentityProvider.token MY_TOKEN --ip 0.0.0.0 ``` > [!NOTE] > If you are running notebooks through JupyterHub instead of JupyterLab as above, you should: > > - Set the environment variable `JUPYTERHUB_ALLOW_TOKEN_IN_URL=1` in the single-user environment. > - Ensure your API token (`MY_TOKEN`) is created with `access:servers` scope in the Hub. ### 3. Configure Your Preferred MCP Client > [!TIP] > > 1. Ensure the `port` of the `DOCUMENT_URL` and `RUNTIME_URL` match those used in the `jupyter lab` command. > > 2. In a basic setup, `DOCUMENT_URL` and `RUNTIME_URL` are the same. `DOCUMENT_TOKEN`, and `RUNTIME_TOKEN` are also the same and is actually the Jupyter Token. > > 3. The `DOCUMENT_ID` parameter specifies the path to the notebook you want to connect to. It should be relative to the directory where JupyterLab was started. > > - **Optional:** If you omit `DOCUMENT_ID`, the MCP client can automatically list all available notebooks on the Jupyter server, allowing you to select one interactively via your prompts. > - **Flexible:** Even if you set `DOCUMENT_ID`, the MCP client can still browse, list, switch to, or even create new notebooks at any time. > #### MacOS and Windows ```json { "mcpServers": { "jupyter": { "command": "docker", "args": [ "run", "-i", "--rm", "-e", "DOCUMENT_URL", "-e", "DOCUMENT_TOKEN", "-e", "DOCUMENT_ID", "-e", "RUNTIME_URL", "-e", "RUNTIME_TOKEN", "-e", "ALLOW_IMG_OUTPUT", "datalayer/jupyter-mcp-server:latest" ], "env": { "DOCUMENT_URL": "http://host.docker.internal:8888", "DOCUMENT_TOKEN": "MY_TOKEN", "DOCUMENT_ID": "notebook.ipynb", "RUNTIME_URL": "http://host.docker.internal:8888", "RUNTIME_TOKEN": "MY_TOKEN", "ALLOW_IMG_OUTPUT": "true" } } } } ``` #### Linux ```json { "mcpServers": { "jupyter": { "command": "docker", "args": [ "run", "-i", "--rm", "-e", "DOCUMENT_URL", "-e", "DOCUMENT_TOKEN", "-e", "DOCUMENT_ID", "-e", "RUNTIME_URL", "-e", "RUNTIME_TOKEN", "-e", "ALLOW_IMG_OUTPUT", "--network=host", "datalayer/jupyter-mcp-server:latest" ], "env": { "DOCUMENT_URL": "http://localhost:8888", "DOCUMENT_TOKEN": "MY_TOKEN", "DOCUMENT_ID": "notebook.ipynb", "RUNTIME_URL": "http://localhost:8888", "RUNTIME_TOKEN": "MY_TOKEN", "ALLOW_IMG_OUTPUT": "true" } } } } ``` For detailed instructions on configuring various MCP clients—including [Claude Desktop](https://jupyter-mcp-server.datalayer.tech/clients/claude_desktop), [VS Code](https://jupyter-mcp-server.datalayer.tech/clients/vscode), [Cursor](https://jupyter-mcp-server.datalayer.tech/clients/cursor), [Cline](https://jupyter-mcp-server.datalayer.tech/clients/cline), and [Windsurf](https://jupyter-mcp-server.datalayer.tech/clients/windsurf) — see the [Clients documentation](https://jupyter-mcp-server.datalayer.tech/clients). ## 📚 Resources Looking for blog posts, videos, or other materials about Jupyter MCP Server? 👉 Visit the [Resources section](https://jupyter-mcp-server.datalayer.tech/resources).