Chrome Bookmark MCP Server

A Model Context Protocol (MCP) server implementation that provides seamless integration between Chrome bookmarks and AI assistants. This server enables AI models to access, search, analyze, and manage Chrome bookmarks through a standardized protocol.

Overview

This project implements an MCP server that bridges Chrome bookmarks with AI assistants, allowing for intelligent bookmark management, search, and analysis. It includes both a Chrome extension for data collection and a Python-based MCP server for processing requests.

Related MCP server: Chrome MCP Server

Features

Core Functionality

• Bookmark Access: Read and search through Chrome bookmarks

• Advanced Search: Full-text search with ElasticSearch integration

• Real-time Sync: Automatic synchronization of bookmark changes via WebSocket

• Analytics: Bookmark usage patterns and insights

• Security: JWT authentication and secure communication

Chrome Extension

• Bookmark export and synchronization

• Search interface with advanced filtering

• Usage analytics dashboard

• Real-time updates via WebSocket

• Offline message queuing

MCP Server

• Standard MCP protocol implementation

• WebSocket support for real-time updates

• RESTful API endpoints

• Redis caching for performance

• ElasticSearch for advanced search

• Docker support for easy deployment

Architecture

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│ Chrome Browser  │────▶│  Chrome Ext.    │────▶│   MCP Server    │
│   (Bookmarks)   │     │  (Data Export)  │     │  (Processing)   │
└─────────────────┘     └─────────────────┘     └─────────────────┘
                                                          │
                                ┌─────────────────────────┴─────────────┐
                                │                                       │
                        ┌───────▼────────┐                    ┌────────▼───────┐
                        │     Redis      │                    │ ElasticSearch  │
                        │   (Caching)    │                    │   (Search)     │
                        └────────────────┘                    └────────────────┘

Prerequisites

• Python 3.8+

• Docker and Docker Compose

• Chrome Browser

• Redis (via Docker)

• ElasticSearch (via Docker)

Installation

1. Clone the Repository

git clone https://github.com/mamba-mental/chrome-bookmark-mcp-server.git
cd chrome-bookmark-mcp-server

2. Set Up the Server

Using Docker (Recommended)

# Start all services
docker-compose up -d

# Check service status
docker-compose ps

Manual Setup

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Set up Redis and ElasticSearch (see docs/REDIS_SETUP.md and docs/ELASTICSEARCH_SETUP.md)

3. Install Chrome Extension

1. Open Chrome and navigate to chrome://extensions/

2. Enable "Developer mode"

3. Click "Load unpacked"

4. Select the chrome-extension folder from this repository

5. The extension icon should appear in your toolbar

4. Configure the Server

Create a .env file in the project root:

# Server Configuration
MCP_SERVER_HOST=localhost
MCP_SERVER_PORT=8012
SECRET_KEY=your-secret-key-here

# Redis Configuration
REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_DB=0

# ElasticSearch Configuration
ELASTICSEARCH_HOST=localhost
ELASTICSEARCH_PORT=9200
ELASTICSEARCH_INDEX=chrome_bookmarks

# Security
JWT_SECRET_KEY=your-jwt-secret-key-here
JWT_ALGORITHM=HS256
JWT_EXPIRATION_DELTA=3600

Usage

Starting the Server

With Docker

docker-compose up

Without Docker

python server/MCP_Chrome_Server_033025.py

Chrome Extension

1. Click the extension icon in Chrome

2. Use the popup interface to:

   • Export bookmarks to the server

   • Search bookmarks with filters

   • View analytics dashboard

   • Configure settings

MCP Integration

Connect your AI assistant to the MCP server:

{
  "mcpServers": {
    "chrome-bookmarks": {
      "command": "python",
      "args": ["/path/to/server/MCP_Chrome_Server_033025.py"],
      "env": {
        "PYTHONPATH": "/path/to/project"
      }
    }
  }
}

API Documentation

MCP Tools

The server provides the following MCP tools:

• search_bookmarks: Search bookmarks with advanced filters

• get_bookmark: Retrieve a specific bookmark by ID

• analyze_bookmarks: Get analytics and insights

• organize_bookmarks: Auto-organize bookmarks

• export_bookmarks: Export bookmarks in various formats

REST API Endpoints

• GET /api/bookmarks: List all bookmarks

• GET /api/bookmarks/search: Search bookmarks

• GET /api/bookmarks/{id}: Get specific bookmark

• POST /api/bookmarks/sync: Sync bookmarks from Chrome

• GET /api/analytics/dashboard: Get analytics data

• POST /api/auth/login: Authenticate and get JWT token

• WS /ws: WebSocket endpoint for real-time updates

Development

Project Structure

chrome-bookmark-mcp-server/
├── chrome-extension/       # Chrome extension source
│   ├── manifest.json      # Extension manifest
│   ├── popup.html/js      # Extension popup interface
│   ├── background.js      # Background service worker
│   ├── search.html        # Search interface
│   ├── analysis.html      # Analytics dashboard
│   └── icons/             # Extension icons
├── server/                # MCP server implementation
│   ├── MCP_Chrome_Server_033025.py         # Main server
│   ├── MCP_Chrome_Schemas_033025.py        # Data schemas
│   ├── Security_Module_033025.py           # Security module
│   ├── Advanced_Features_Module_033025.py  # Advanced features
│   └── requirements.txt                    # Python dependencies
├── config/                # Configuration files
├── docs/                  # Documentation
│   ├── ELASTICSEARCH_SETUP.md
│   ├── REDIS_SETUP.md
│   └── MCP_Implementation_Project_Master_Plan_031125.md
├── docker-compose.yml     # Docker configuration
└── requirements.txt       # Root Python dependencies

Running Tests

# Run unit tests
python -m pytest tests/

# Run with coverage
python -m pytest --cov=server tests/

Contributing

1. Fork the repository

2. Create a feature branch (git checkout -b feature/amazing-feature)

3. Commit your changes (git commit -m 'Add amazing feature')

4. Push to the branch (git push origin feature/amazing-feature)

5. Open a Pull Request

Security Considerations

• JWT tokens for authentication

• API rate limiting to prevent abuse

• Secure WebSocket connections (WSS in production)

• Input validation and sanitization

• No storage of sensitive user data

Troubleshooting

Common Issues

1. Extension not connecting to server

   • Check server is running on port 8012

   • Verify no firewall blocking

   • Check browser console for errors

2. Search not working

   • Ensure ElasticSearch is running

   • Check if bookmarks are indexed

   • Verify ElasticSearch connection

3. WebSocket disconnections

   • Check network stability

   • Review server logs

   • Ensure proper CORS configuration

For detailed setup instructions, see:

• Installation Guide

• ElasticSearch Setup

• Redis Setup

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Model Context Protocol specification by Anthropic

• Chrome Extensions API documentation

• Open source libraries and contributors

Contact

For questions, issues, or contributions, please open an issue on GitHub.

Note: This is an active development project. Features and APIs may change. Please refer to the latest documentation and release notes.