Сервер Jupyter MCP

🪐✨ Jupyter MCP Server

🚨 BREAKING CHANGE For version 0.11.0+, room has been renamed to document. Read more in the release notes.

Jupyter MCP Server is a Model Context Protocol (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 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.
🤝 MCP-Compatible: Works with any MCP client, such as Claude Desktop, Cursor, Windsurf, and more.

Jupyter MCP Server Demo

🛠️ This MCP offers multiple tools such as insert_execute_code_cell, append_markdown_cell, get_notebook_info, read_cell, and more, enabling advanced interactions with Jupyter notebooks. Explore our tools documentation 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. Or, get started quickly with JupyterLab and stdio transport here below.

1. Set Up Your Environment

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

# make jupyterlab
jupyter lab --port 8888 --IdentityProvider.token MY_TOKEN --ip 0.0.0.0

3. Configure Your Preferred MCP Client

Note

Ensure the port of the DOCUMENT_URL and RUNTIME_URL match those used in the jupyter lab command.

The DOCUMENT_ID which is the path to the notebook you want to connect to, should be relative to the directory where JupyterLab was started.

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.

MacOS and Windows

{
  "mcpServers": {
    "jupyter": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "DOCUMENT_URL",
        "-e",
        "DOCUMENT_TOKEN",
        "-e",
        "DOCUMENT_ID",
        "-e",
        "RUNTIME_URL",
        "-e",
        "RUNTIME_TOKEN",
        "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"
      }
    }
  }
}

Linux

{
  "mcpServers": {
    "jupyter": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "DOCUMENT_URL",
        "-e",
        "DOCUMENT_TOKEN",
        "-e",
        "DOCUMENT_ID",
        "-e",
        "RUNTIME_URL",
        "-e",
        "RUNTIME_TOKEN",
        "--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"
      }
    }
  }
}

For detailed instructions on configuring various MCP clients—including Claude Desktop, VS Code, Cursor, Cline, and Windsurf — see the Clients documentation.

📚 Resources

Looking for blog posts, videos, or other materials about Jupyter MCP Server?

👉 Visit the Resources section.

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Обеспечивает взаимодействие с блокнотами Jupyter через протокол контекста модели, поддерживая выполнение кода и вставку разметки в средах JupyterLab.

Related Resources

Reddit Discussion about this server

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Jupyter MCP Server

🪐✨ Jupyter MCP Server

🚀 Key Features

🏁 Getting Started

1. Set Up Your Environment

2. Start JupyterLab

3. Configure Your Preferred MCP Client

MacOS and Windows

Linux

📚 Resources

Related Resources

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