What is Mcp Jupyter

mcp-jupyter is a Jupyter MCP Server that allows users to utilize tools like Goose or Cursor in a JupyterLab notebook, preserving the state of variables through the JupyterLab Kernel.

Use cases

Use cases include collaborative data analysis, automated package installation during coding sessions, and interactive exploration of datasets with the assistance of an agent.

How to use

To use mcp-jupyter, ensure that a server is running on an available port, set up the environment with required packages, and start JupyterLab. Use the command uvx mcp-jupyter to add it to your client.

Key features

Key features include state preservation of variables, integration with tools like Goose, and the ability to seamlessly hand off tasks between the user and the agent.

Where to use

mcp-jupyter can be used in data science, machine learning, and collaborative coding environments where real-time data exploration and analysis are required.

Jupyter MCP Server

Jupyter MCP Server allows you to use tools like Goose or Cursor to pair with you in a JupyterLab notebook where the state of your variables, etc is preserved by the Jupterlab Kernel. The fact that state is preserved is the key to this because it allows to to pair with the Agent in a notebook, where for example if a package is not installed it will see the error and install it for you. You as the user can then do some data exploration and then hand off to the agent at any time to pick up where you left off.

This works with any client that supports MCP but will focus on using Goose for the examples.

Requirements

You will need UV is required to be installed.

Installation

This MCP server uses stdio and can been added to client with the command uvx mcp-jupyter.

Usage

Start Jupyter

The server expects that a server is already running on a port that is available to the client. If the environmental variable TOKEN is not set, it will default to “BLOCK”. The server requires that jupyter-collaboration and ipykernel are installed. An example setup is below.

uv venv
source .venv/bin/activate
uv pip install jupyterlab jupyter-collaboration ipykernel
jupyter lab --port 8888 --IdentityProvider.token BLOCK --ip 0.0.0.0

Goose Usage

Here’s a demonstration of the tool in action:

You can view the Generated notebook here: View Demo Notebook

Development

Steps remain similar except you will need to clone this mcp-jupyter repository and use that for the server instead of the precompiled version.

MCP Server

1. Clone and setup the repository:

mkdir ~/Development
cd ~/Development
git clone https://github.com/block/mcp-jupyter.git
cd mcp-server

uv venv
source .venv/bin/activate
uv pip install -e .

Using editable mode allows you to make changes to the server and only have you need to restart Goose, etc.
goose session --with-extension "uv run $(pwd)/.venv/bin/mcp-jupyter"