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🪐🔧 Jupyter MCP Server
An MCP server developed for AI to connect and manage Jupyter Notebooks in real-time — and scale your Code Sandbox from local to the cloud (Datalayer, Kaggle, Google Colab, Modal, Daytona, E2B, CoreWeave, Cloudflare...)
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📖 Documentation · 🔧 Tools · 💬 Community
No process to run. Datalayer now hosts this server for you at
https://mcp.datalayer.run/mcp — one endpoint for every agent and every notebook.
Sign in from your browser, approve what the agent may do, and your work keeps running
on the server after the agent disconnects.
One command to connect Claude Code, with /datalayer:notebook, /datalayer:run and
/datalayer:status on top:
/plugin marketplace add datalayer/jupyter-mcp-server
/plugin install datalayer→ Datalayer plugin for Claude Code
Free and open source, BSD 3-Clause — point it at any Jupyter you already run, local or JupyterHub, no account needed.
Built and maintained by Datalayer, where the same server drives always-on Notebooks with GPU Code Sandboxes and durable execution — so your agent keeps working on your data when your laptop does not.
No token to copy and paste. An agent that meets this server unauthenticated is told where to authenticate, opens your browser, and you sign in to Datalayer as yourself. The agent never sees your password — it receives a token scoped to what you approved, and you can disconnect one agent without touching the others.
What each agent may do is two separate decisions: the scopes you approve
(notebooks:read, notebooks:write, code:execute, data:read) say what kind of
operation it may perform, and your own Datalayer permissions still say which notebooks it
may touch. An agent can never reach a notebook you cannot.
Personal access tokens keep working, and remain the simpler path for a CLI or a script. → OAuth and identity
Pin code-sandboxes to match your jupyter-mcp-server. The sandbox variant
jupyter was renamed to jupyter-server in code-sandboxes 1.1.1, and the two packages
have to agree on the name.
Your jupyter-mcp-server | Install |
|---|---|
| >= 1.5.0 | code-sandboxes >= 1.1.1 |
| < 1.5.0 | code-sandboxes <= 1.0.9 |
# On 1.5.0 or later
pip install "jupyter-mcp-server>=1.5.0" "code-sandboxes>=1.1.1"
# Staying on an earlier jupyter-mcp-server
pip install "jupyter-mcp-server<1.5.0" "code-sandboxes<=1.0.9"An older server with a newer code-sandboxes installs cleanly and then fails on the
first execution with Unknown sandbox variant: jupyter.
→ Release notes
--provider is now --document-provider (env var PROVIDER → DOCUMENT_PROVIDER).
It only ever chose where the notebook documents live — jupyter for the collaboration
API of a Jupyter Server, datalayer for the Datalayer spacer — while the old name and its
help text suggested it also chose where code runs. Execution is picked separately, with
--sandbox-variant (jupyter-server, datalayer, daytona, e2b, coreweave,
cloudflare, kaggle, google-colab, monty, modal).
Nothing breaks in v1.3.2: --provider is still accepted as an alias, PROVIDER is still
read, and a /connect payload carrying "provider" is still understood. Move to the new
names when convenient — the old ones are deprecated, not removed.
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📖 Table of Contents
🚀 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.
- 🧠 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, Windsurf, and more.
- 🔍 Observability: Built-in hook system with OpenTelemetry integration for tracing tool calls and kernel executions.
Compatible with any Jupyter deployment (local, JupyterHub, ...) and with Datalayer hosted Notebooks, where the Code Sandboxes come with GPUs and the execution survives a disconnect.
🔧 MCP Overview
🔧 Tools Overview
The server provides a rich set of tools for interacting with Jupyter notebooks, categorized as follows. For more details on each tool, their parameters, and return values, please refer to the official Tools documentation.
Server and Code Sandbox Management Tools
| Name | Description |
|---|---|
list_files | List files and directories in the Jupyter server's file system. |
list_kernels | List all available and running kernel sessions on the Jupyter server. |
launch_sandbox | Launch a code sandbox (eval/docker/jupyter-server/datalayer/daytona/e2b/coreweave/cloudflare/kaggle/google-colab/monty/modal) as an alternative execution backend for execute_code. Supports variant-specific options including GPU flavor for supported backends. Requires the jupyter_mcp_sandboxes extension. |
list_sandboxes | List launched code sandboxes and their state (active flag, variant, status, and selected code sandbox options). Requires the jupyter_mcp_sandboxes extension. |
use_sandbox | Select or clear the active sandbox used by execute_code, enabling dynamic routing between kernel-backed and sandbox-backed execution. Requires the jupyter_mcp_sandboxes extension. |
terminate_sandbox | Stop and unregister a launched code sandbox. Requires the jupyter_mcp_sandboxes extension. |
connect_to_jupyter | Connect to a Jupyter server dynamically without restarting the MCP server. Not available when running as Jupyter extension. Useful for switching servers dynamically or avoiding hardcoded configuration. |
Multi-Notebook Management Tools
| Name | Description |
|---|---|
use_notebook | Connect to a notebook file, create a new one, or switch between notebooks. |
list_notebooks | List all notebooks available on the Jupyter server and their status |
restart_notebook | Restart the kernel for a specific managed notebook. |
unuse_notebook | Disconnect from a specific notebook and release its resources. |
…