An MCP server for AI agents to interact with Jupyter notebooks
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# MCP Jupyter Notebook Server [](../../LICENSE) A [Model Context Protocol](https://modelcontextprotocol.io/) (MCP) server that gives AI agents full control over a live Jupyter notebook session — run code, add markdown, manage packages, inspect variables, and more. Built on [agent-jupyter-toolkit](https://github.com/Cyb3rWard0g/agent-jupyter-toolkit) and the [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk). <p align="center"> <img src="https://img.shields.io/badge/MCP-Tools-green" alt="MCP tools" /> <img src="https://img.shields.io/badge/transport-stdio%20|%20SSE%20|%20HTTP-orange" alt="transports" /> <img src="https://img.shields.io/badge/python-3.11%2B-blue" alt="Python 3.11+" /> </p> ## Features - **Rich MCP tool surface** — notebook lifecycle, code execution, markdown, stable cell-ID operations, packages, kernel control, introspection, variables - **Optional PostgreSQL tools** — enable on demand to create a DB client/connection in the kernel - **Optional Azure Monitor Logs / Sentinel tools** — visible KQL query cells, reusable kernel clients and retained DataFrames; [setup and result semantics](../../docs/mcp-server/azure-monitor.md) - **Real-time sync** — Yjs collaboration transport shows cell edits instantly in JupyterLab - **Two session modes** — connect to a remote Jupyter server or run a local kernel - **Three transports** — stdio (for editors), SSE, or streamable HTTP - **MCP SDK compatibility** — supports the 1.26 API and the 2.x `MCPServer` API - **Observable collaboration policy** — required, preferred with classified fallback, or disabled - **Zero config defaults** — sensible defaults with full override via CLI args or env vars ## Quick Start ### 1. Start a Jupyter server ```sh # With Docker (recommended — includes jupyter-collaboration) cd quickstarts && docker compose up -d --build # Or locally pip install jupyterlab ipykernel jupyter-collaboration …
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