Jupyter Notebook MCP Server

A VS Code / Cursor extension that exposes Jupyter notebook manipulation via MCP (Model Context Protocol): read/edit/run cells and capture outputs. Works with Claude Code, Cursor Agent, Windsurf, and any MCP-compatible AI assistant.

Install: VS Code Marketplace · Open VSX · GitHub

[!IMPORTANT] This project is still pre-alpha so it's very rough on the edge. Working in multiple windows is unstable.

Why connect to VS Code Runtime API?

There are currently two main architectures to give AI agents access to Jupyter notebooks. This project was heavily inspired by both - kudos to these teams for pioneering the space:

Architecture 1: File-based (e.g., cursor-notebook-mcp)

These servers read/write .ipynb files directly using libraries like nbformat.

Pros: No server dependencies, works out-of-the-box

Cons:

• Cannot execute code - agents can only edit cells, user must run them manually
• UI sync issues - VS Code may show stale content until you revert/reopen
• The .ipynb JSON format is verbose (~3x more tokens than raw code)
• Race conditions if you edit while agent writes

Architecture 2: Jupyter Server API (e.g., jupyter-mcp-server)

These servers connect to Jupyter's REST API and can execute code through the kernel.

Pros: Can execute code, best choice for standalone remote JupyterLab/JupyterHub deployments

Cons:

• Requires running JupyterLab separately (jupyter lab --port 8888)
• Auth setup: tokens, URLs, environment variables
• You end up running two UIs: one for notebook and another for AI
• If you open the notebook in VS Code you create another source of truth (Jupyter server state vs your editor)

Architecture 3: VS Code / Cursor Runtime API (this extension)

We're introducing a third architecture - hooking directly into Cursor/VS Code's Notebook API, the same API the editor uses internally.

Pros:

• Zero config - just install, server starts automatically
• Faster reads (direct memory access, no serialization)
• Executes code in your existing kernel (the one VS Code already manages)
• Changes appear instantly in the editor with full undo/redo support
• Single source of truth: what you see is what the agent sees
• Works with remote kernels - if VS Code connects to a remote Jupyter server, so does the agent

Cons:

• Only works inside VS Code / Cursor (won't help if you use JupyterLab web UI)

When to use what

Use case	Recommended
VS Code / Cursor + AI coding	This extension
Remote VS Code / Cursor (tunnels, containers, SSH)	This extension
Standalone JupyterLab/JupyterHub server	Datalayer
Just edit cells, no execution needed	File-based

Features

• Execute code in the active kernel and retrieve outputs
• Full cell manipulation - insert, edit, delete, move cells
• Read cell contents and outputs including images (base64)
• Search and navigate - find text, get notebook outline
• Bulk operations - add multiple cells, clear all outputs

Tools (15)

Navigation & Reading

Tool	Description
notebook_list_open	List all open notebooks with URIs and cell counts
notebook_list_cells	List cells with type, language, preview, execution state
notebook_get_cell_content	Get full source code of a cell
notebook_get_cell_output	Get cell outputs (text, errors, images as base64)
notebook_get_outline	Get notebook structure (headings, functions, classes)
notebook_search	Search all cells for a keyword with context
notebook_get_kernel_info	Get kernel name, language, and state

Cell Manipulation

Tool	Description
notebook_insert_cell	Insert a code or markdown cell at any position
notebook_edit_cell	Replace the content of an existing cell
notebook_delete_cell	Delete a cell by index
notebook_move_cell	Move a cell to a different position
notebook_bulk_add_cells	Add multiple cells in a single operation

Execution & Outputs

To execute ad-hoc code, use notebook_insert_cell with execute: true. To execute an existing cell, use notebook_run_cell.

Tool	Description
notebook_run_cell	Execute an existing code cell by index and return outputs
notebook_clear_outputs	Clear outputs of a specific cell
notebook_clear_all_outputs	Clear outputs from all cells

<details> <summary><b>Tool Parameters</b></summary>

All tools support response_format parameter ("markdown" or "json").

notebook_insert_cell

{
  "content": "print('hello')",
  "type": "code",
  "index": 0,
  "language": "python",
  "execute": false
}

notebook_edit_cell

{
  "index": 0,
  "content": "# New content"
}

notebook_search

{
  "query": "import pandas",
  "case_sensitive": false,
  "context_lines": 1
}

notebook_move_cell

{
  "from_index": 5,
  "to_index": 0
}

notebook_bulk_add_cells

{
  "cells": [
    {"content": "# Header", "type": "markdown"},
    {"content": "x = 1", "type": "code", "language": "python"}
  ],
  "index": 0
}

notebook_run_cell

{
  "index": 0
}

</details>

Setup

1. Install the extension in VS Code or Cursor
2. Add to your MCP client config:

{
  "mcpServers": {
    "notebook": {
      "url": "http://127.0.0.1:49777/mcp"
    }
  }
}

[!ATTENTION] The server starts automatically when VS Code / Cursor opens. Look for the 🪐 :49777 indicator in the status bar.

Configuration

Setting	Default	Description
notebook-mcp.port	49777	Port number for the MCP server

Performance

Tested with a 471-cell notebook (~2.8MB, 1MB outputs):

Operation	Time
List/read cells	<1ms
Search all cells	<1ms
Generate outline	~1ms
Insert/edit cell	~7ms

[!NOTE] Read operations are sub-millisecond because they access in-memory data structures directly. Write operations (~7ms) go through VS Code's edit pipeline for undo/redo support.

Requirements

• VS Code 1.85+ or Cursor 0.43+
• Jupyter extension

Architecture

┌─────────────────────────────────────────────────────────────────────────┐
│                         VS Code / Cursor                                │
│                                                                         │
│  ┌───────────────────────────────────────────────────────────────────┐  │
│  │                    Jupyter Extension                              │  │
│  │                                                                   │  │
│  │   Notebook Document  ◄───►  Kernel (Python)  ───►  Outputs        │  │
│  │                                    ▲                              │  │
│  └────────────────────────────────────┼──────────────────────────────┘  │
│                                       │                                 │
│  ┌────────────────────────────────────┼──────────────────────────────┐  │
│  │              Notebook MCP Server Extension                        │  │
│  │                                    │                              │  │
│  │   ┌────────────────────────────────┴───────────────────────────┐  │  │
│  │   │                  HTTP Server (:49777)                      │  │  │
│  │   │                                                            │  │  │
│  │   │  execute_code  insert_cell  list_cells  get_output  ...    │  │  │
│  │   └────────────────────────────────────────────────────────────┘  │  │
│  └───────────────────────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────────────────────┘
                                    │
                                    │ HTTP (MCP Protocol)
                                    ▼
                    ┌───────────────────────────────┐
                    │           AI Agent            │
                    │   (Claude Code, Cursor, etc)  │
                    └───────────────────────────────┘

How It Works

1. Extension embeds an HTTP-based MCP server (port 49777)
2. AI agent (Claude Code, Cursor Agent, etc.) sends tool calls via MCP protocol
3. Server uses VS Code / Cursor APIs to manipulate the active notebook
4. Changes appear instantly in the editor
5. Outputs are captured and returned to the agent

This enables true interactive notebook sessions with AI agents in VS Code and Cursor.