Jupyter Server MCP Extension

A configurable MCP (Model Context Protocol) server extension for Jupyter Server that allows dynamic registration of Python functions as tools accessible to MCP clients from a running Jupyter Server.

https://github.com/user-attachments/assets/aa779b1c-a443-48d7-b3eb-13f27a4333b3

Overview

This extension provides a simplified, trait-based approach to exposing Jupyter functionality through the MCP protocol. It can dynamically load and register tools from various Python packages, making them available to AI assistants and other MCP clients.

Key Features

• Simplified Architecture: Direct function registration without complex abstractions
• Configurable Tool Loading: Register tools via string specifications (module:function)
• Automatic Tool Discovery: Python packages can expose tools via entrypoints
• Jupyter Integration: Seamless integration with Jupyter Server extension system
• HTTP Transport: FastMCP-based HTTP server with proper MCP protocol support
• Traitlets Configuration: Full configuration support through Jupyter's traitlets system

Installation

pip install jupyter-server-mcp

Quick Start

1. Basic Configuration

Create a jupyter_config.py file:

c = get_config()

# Basic MCP server settings
c.MCPExtensionApp.mcp_name = "My Jupyter MCP Server"
c.MCPExtensionApp.mcp_port = 8080

# Register tools from existing packages
c.MCPExtensionApp.mcp_tools = [
    # Standard library tools
    "os:getcwd",
    "json:dumps",
    "time:time",
    
    # Jupyter AI Tools - Notebook operations  
    "jupyter_ai_tools.toolkits.notebook:read_notebook",
    "jupyter_ai_tools.toolkits.notebook:edit_cell",
    
    # JupyterLab Commands Toolkit
    "jupyterlab_commands_toolkit.tools:list_all_commands",
    "jupyterlab_commands_toolkit.tools:execute_command",
]

2. Start Jupyter Server

jupyter lab --config=jupyter_config.py

The MCP server will start automatically on http://localhost:8080/mcp.

3. Connect MCP Clients

Claude Code Configuration:

Set the following configuration:

"mcpServers": {
  "jupyter-mcp": {
    "type": "http",
    "url": "http://localhost:8085/mcp"
  }
}

Or use the claude CLI:

claude mcp add --transport http jupyter-mcp http://localhost:8080/mcp

Gemini CLI Configuration:

Add the following to .gemini/settings.json:

{
  "mcpServers": {
    "jupyter-mcp": {
      "httpUrl": "http://localhost:8080/mcp"
    }
  }
}

Architecture

Core Components

MCPServer (jupyter_server_mcp.mcp_server.MCPServer)

A simplified LoggingConfigurable class that manages FastMCP integration:

from jupyter_server_mcp.mcp_server import MCPServer

# Create server
server = MCPServer(name="My Server", port=8080)

# Register functions
def my_tool(message: str) -> str:
    return f"Hello, {message}!"

server.register_tool(my_tool)

# Start server
await server.start_server()

Key Methods:

• register_tool(func, name=None, description=None) - Register a Python function
• register_tools(tools) - Register multiple functions (list or dict)
• list_tools() - Get list of registered tools
• start_server(host=None) - Start the HTTP MCP server

MCPExtensionApp (jupyter_server_mcp.extension.MCPExtensionApp)

Jupyter Server extension that manages the MCP server lifecycle:

Configuration Traits:

• mcp_name - Server name (default: "Jupyter MCP Server")
• mcp_port - Server port (default: 3001)
• mcp_tools - List of tools to register (format: "module:function")
• use_tool_discovery - Enable automatic tool discovery via entrypoints (default: True)

Tool Registration

Tools can be registered in two ways:

1. Manual Configuration

Specify tools directly in your Jupyter configuration using module:function format:

c.MCPExtensionApp.mcp_tools = [
    "os:getcwd",
    "jupyter_ai_tools.toolkits.notebook:read_notebook",
]

... [View full README on GitHub](https://github.com/jupyter-ai-contrib/jupyter-server-mcp#readme)