datalayer/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...)**
*Developed by Datalayer - Join our Discord*
📖 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.
→ **Hosted Jupyter MCP Server**
**One command to connect Claude Code**, with /datalayer:notebook, /datalayer:run and
/datalayer:status on top:
→ **Datalayer plugin for Claude Code**
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**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.
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**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>=1.1.1"
Staying on an earlier jupyter-mcp-server
pip install "jupyter-mcp-server =2, = 2.0.0** | mcp >= 2 |
| **
📖 Table of Contents
- Key Features
- MCP Overview
- Getting Started
- Sandbox Variants
- Best Practices
- Contributing
- Resources
🚀 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
Every tool, with its parameters, schema and return value, is generated from a live
snapshot of the running server and published at
**jupyter-mcp-server.datalayer.tech/mcp** — so it is never out of step with the
code, which a table copied into this file would be.
They fall into four groups:
- **Server and code sandbox** — browse the Jupyter file system, list kernels, connect to
a server at runtime, and launch, select and terminate code sandboxes.