🪐✨ Jupyter MCP Server
🚨 BREAKING CHANGE For version
0.11.0+
,room
has been renamed todocument
. 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.
🛠️ 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
2. Start JupyterLab
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
Linux
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
hybrid server
The server is able to function both locally and remotely, depending on the configuration or use case.
Ermöglicht die Interaktion mit Jupyter-Notebooks über das Model Context Protocol und unterstützt die Codeausführung und das Einfügen von Markdowns in JupyterLab-Umgebungen.
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