Jupyter Notebook MCP server for AI agents

Jupyter MCP Server is a specialized server that enables AI to connect with and manage Jupyter Notebooks in real-time, allowing for seamless interaction with notebook content and execution. It implements the Model Context Protocol (MCP) to provide a standardized interface for AI assistants to work with Jupyter environments.

Installation

Prerequisites

Before installing, make sure you have the following components:

pip install jupyterlab==4.4.1 jupyter-collaboration==4.0.2 jupyter-mcp-tools>=0.1.4 ipykernel
pip uninstall -y pycrdt datalayer_pycrdt
pip install datalayer_pycrdt==0.12.17

Starting JupyterLab

Start a JupyterLab server that the MCP server will connect to:

jupyter lab --port 8888 --IdentityProvider.token MY_TOKEN --ip 0.0.0.0

If you're using JupyterHub instead, set the environment variable JUPYTERHUB_ALLOW_TOKEN_IN_URL=1 and ensure your API token has the access:servers scope.

Configuration

Configure your MCP client to connect to the server using one of these methods:

Using uvx (Quick Setup)

First, install uv:

pip install uv

Configure your client with the following settings:

{
  "mcpServers": {
    "jupyter": {
      "command": "uvx",
      "args": ["jupyter-mcp-server@latest"],
      "env": {
        "JUPYTER_URL": "http://localhost:8888",
        "JUPYTER_TOKEN": "MY_TOKEN",
        "ALLOW_IMG_OUTPUT": "true"
      }
    }
  }
}

Using Docker (Production Setup)

For macOS and Windows:

{
  "mcpServers": {
    "jupyter": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-e", "JUPYTER_URL",
        "-e", "JUPYTER_TOKEN",
        "-e", "ALLOW_IMG_OUTPUT",
        "datalayer/jupyter-mcp-server:latest"
      ],
      "env": {
        "JUPYTER_URL": "http://host.docker.internal:8888",
        "JUPYTER_TOKEN": "MY_TOKEN",
        "ALLOW_IMG_OUTPUT": "true"
      }
    }
  }
}

For Linux:

{
  "mcpServers": {
    "jupyter": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-e", "JUPYTER_URL",
        "-e", "JUPYTER_TOKEN",
        "-e", "ALLOW_IMG_OUTPUT",
        "--network=host",
        "datalayer/jupyter-mcp-server:latest"
      ],
      "env": {
        "JUPYTER_URL": "http://localhost:8888",
        "JUPYTER_TOKEN": "MY_TOKEN",
        "ALLOW_IMG_OUTPUT": "true"
      }
    }
  }
}

Available Tools

The MCP server provides a comprehensive set of tools for interacting with Jupyter notebooks:

Server Management Tools

list_files: List files and directories in the Jupyter server's file system
list_kernels: List all available and running kernel sessions

Multi-Notebook Management Tools

use_notebook: Connect to a notebook file, create a new one, or switch between notebooks
list_notebooks: List all notebooks available and their status
restart_notebook: Restart the kernel for a specific notebook
unuse_notebook: Disconnect from a notebook and release resources
read_notebook: Read notebook cells with brief or detailed format options

Cell Operations and Execution Tools

read_cell: Read the full content of a single cell
insert_cell: Insert a new code or markdown cell
delete_cell: Delete a cell at a specified index
overwrite_cell_source: Overwrite the source code of a cell
execute_cell: Execute a cell with support for multimodal output
insert_execute_code_cell: Insert and execute a new code cell in one step
execute_code: Execute code directly in the kernel

JupyterLab Integration

notebook_run-all-cells: Execute all cells in the current notebook sequentially

Prompt Features

The server also supports MCP prompt features for enhanced interaction:

jupyter-cite: Cite specific cells from a notebook (similar to @ in code editors)

Best Practices

For optimal use of the Jupyter MCP server:

Use LLMs with multimodal input support (like Gemini 2.5 Pro) to fully leverage capabilities
Choose an MCP client that supports and can parse image data
Break complex data science workflows into smaller, focused tasks
Provide structured prompts with clear instructions
Include as much context as possible about your environment and requirements

How to install this MCP server

For Claude Code

To add this MCP server to Claude Code, run this command in your terminal:

claude mcp add-json "jupyter" '{"command":"docker","args":["run","-i","--rm","-e","SERVER_URL","-e","TOKEN","-e","NOTEBOOK_PATH","datalayer/jupyter-mcp-server:latest"],"env":{"SERVER_URL":"http://host.docker.internal:8888","TOKEN":"MY_TOKEN","NOTEBOOK_PATH":"notebook.ipynb"}}'

See the official Claude Code MCP documentation for more details.

For Cursor

There are two ways to add an MCP server to Cursor. The most common way is to add the server globally in the ~/.cursor/mcp.json file so that it is available in all of your projects.

If you only need the server in a single project, you can add it to the project instead by creating or adding it to the .cursor/mcp.json file.

Adding an MCP server to Cursor globally

To add a global MCP server go to Cursor Settings > Tools & Integrations and click "New MCP Server".

When you click that button the ~/.cursor/mcp.json file will be opened and you can add your server like this:

{
    "mcpServers": {
        "jupyter": {
            "command": "docker",
            "args": [
                "run",
                "-i",
                "--rm",
                "-e",
                "SERVER_URL",
                "-e",
                "TOKEN",
                "-e",
                "NOTEBOOK_PATH",
                "datalayer/jupyter-mcp-server:latest"
            ],
            "env": {
                "SERVER_URL": "http://host.docker.internal:8888",
                "TOKEN": "MY_TOKEN",
                "NOTEBOOK_PATH": "notebook.ipynb"
            }
        }
    }
}

Adding an MCP server to a project

To add an MCP server to a project you can create a new .cursor/mcp.json file or add it to the existing one. This will look exactly the same as the global MCP server example above.

How to use the MCP server

Once the server is installed, you might need to head back to Settings > MCP and click the refresh button.

The Cursor agent will then be able to see the available tools the added MCP server has available and will call them when it needs to.

You can also explicitly ask the agent to use the tool by mentioning the tool name and describing what the function does.

For Claude Desktop

To add this MCP server to Claude Desktop:

1. Find your configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json

2. Add this to your configuration file:

{
    "mcpServers": {
        "jupyter": {
            "command": "docker",
            "args": [
                "run",
                "-i",
                "--rm",
                "-e",
                "SERVER_URL",
                "-e",
                "TOKEN",
                "-e",
                "NOTEBOOK_PATH",
                "datalayer/jupyter-mcp-server:latest"
            ],
            "env": {
                "SERVER_URL": "http://host.docker.internal:8888",
                "TOKEN": "MY_TOKEN",
                "NOTEBOOK_PATH": "notebook.ipynb"
            }
        }
    }
}

3. Restart Claude Desktop for the changes to take effect