Overview
🚨 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.
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. Open an issue to discuss adding your solution as provider.
Key features include:
- ⚡ 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.
- 🎛️ JupyterLab integration: Enhanced UI integration like automatic notebook opening.
- 🤝 MCP-compatible: Works with any MCP client, such as Claude Desktop, Cursor, Cline, Windsurf and more.
To use Jupyter MCP Server, you first need to decide which setup fits your needs:
- Editor: Do you want to interact with notebooks in Jupyter or with Datalayer hosted Notebooks?
- MCP Transport: Do you want to set up the MCP Server using standard input/output (STDIO) transport or Streamable HTTP transport?
- MCP Server Location: Do you want to set up the MCP Server as a Standalone Server or as a Jupyter Server Extension?
Navigate to the relevant section in the Deployment page to get started based on your needs.
Looking for blog posts, videos or other resources related to Jupyter MCP Server?
👉 Check out the Resources section.
🧰 Dive into the Tools section to understand the tools powering the server.
