Bookmark Geni MCP Server

Stand-alone MCP (Model Context Protocol) server for processing local browser bookmarks and making url/bookmarks easily searchable using natural language.

Connect to Claud Desk top or Gemini CLI Screenshot 2025-11-28 at 10 16 08 PM

Features

🔍 Multi-Browser Support: Reads bookmarks from Chrome, Edge, Firefox, Opera, ChatGPT Atlas, and Perplexity Comet
📄 Content Extraction: Fetches HTML content from URLs and extracts text for semantic search
🏷️ Metadata Extraction: Extracts descriptions from HTML metadata tags (Open Graph, meta description, title)
📊 Vector Storage: Stores bookmark embeddings in ChromaDB using sentence transformer model (all-MiniLM-L6-v2)
🔎 RAG Search: Query bookmarks using natural language with metadata filtering
📦 Portability: Export and import embeddings to/from pickle files for easy transfer
⚡ Performance: Batch processing with concurrency and caching

Installation

Install dependencies:
```
pip install -r requirements.txt
```
Configure the server by editing config.yaml (optional - defaults are provided)
Make start script executable:
```
chmod +x scripts/start_mcp_server.sh
```

Usage

Once the server is started, it can be used with any MCP client. To index all browser bookmarks and generate metadata, run the following command:

"Generate metadata for chrome bookmarks"

Once all bookmarks are indexed, you can query them using the following command:

"Query bookmarks for 'python'"

Standalone MCP Server

The server can be used independently with any MCP client by referencing mcp.json:

# Start the server using the bash script
./scripts/start_mcp_server.sh

With Gemini CLI

Connect the server to the Gemini CLI using the following command:

# Add the server configuration to your Gemini CLI settings
# Edit ~/.gemini/settings.json and add the following to the "mcpServers" section:

{
  "mcpServers": {
    "bookmarkGeni": {
      "command": "bash",
      "args": ["/path/to/bookmark_geni_mcp/scripts/start_mcp_server.sh"],
      "env": {
        "PYTHON_PATH": "/path/to/your/python3"
      }
    }
  }
}

# Or use the provided mcp.json as a reference for the configuration

With Calude Desktop

Connect the server to the Calude Desktop by adding the following to the Calude Desktop settings:

{
  "mcpServers": {
    "bookmarkGeni": {
      "command": "bash",
      "args": ["/path/to/bookmark_geni_mcp/scripts/start_mcp_server.sh"],
      "env": {
        "PYTHON_PATH": "/usr/bin/python3"
      }
    }
  }
}

Note: Replace /path/to/bookmark_geni_mcp with the actual path to this repository and /path/to/your/python3 with your Python interpreter path.

Configuration

The server reads configuration from config.yaml in the MCP server root directory. This includes:

Browser enable/disable settings
ChromaDB path (relative to MCP server root or absolute path)
Metadata JSONL path (relative to MCP server root or absolute path)
URL processing limit (default: -1, meaning process all URLs)
Debug mode

Example config.yaml:

debug: false
browsers:
  Chrome:
    enabled: true
  Edge:
    enabled: true
    # Optional: Override default path detection
    # paths:
    #   - "/path/to/custom/Bookmarks"
chromaDbPath: ".chromadb"
metadataJsonlPath: "data/bookmarks_metadata.jsonl"
urlLimit: -1  # -1 means process all, set to positive number to limit

The server is now completely independent and does not require the Gemini CLI extension folder.

Browser Support

The server supports the following browsers:

Chrome: Windows, macOS, Linux
Edge: Windows, macOS, Linux
Firefox: Windows, macOS, Linux
Opera: Windows, macOS, Linux
ChatGPT Atlas: macOS (Chromium-based)
Perplexity Comet: Windows, macOS, Linux (Chromium-based)

Note: Safari is not supported because reading Bookmarks.plist requires special macOS permissions that are not granted by default. To use Safari bookmarks, you would need to grant Full Disk Access permissions to the Python interpreter, which is not recommended for security reasons.

Tools

The server provides the following MCP tools:

generate_bookmarks_metadata
- Scans selected browsers for bookmarks
- Fetches HTML content and generates metadata
- Creates embeddings and stores them in ChromaDB
- Parameters: browsers (e.g., "Chrome,Safari" or "All")
query_bookmarks
- Performs semantic search on stored bookmarks
- Supports metadata filtering
- Parameters:
  - query: Search text
  - limit: Max results (default 10)
  - where: Filter dict (e.g., {"folder": "Work"})
list_browsers
- Lists installed browsers and their detected bookmark file paths
- Parameters: None
get_stats
- Returns database statistics (total count, collection info)
- Parameters: None
export_embeddings
- Exports all data to a pickle file for backup or transfer
- Parameters: pickle_path (optional)
import_embeddings
- Imports data from a pickle file
- Parameters: pickle_path (required)

See mcp.json for detailed schema definitions.

Workflow

flowchart TD
    %% Nodes
    User([User / CLI])
    Server[MCP Server]
    Detector[Browser Detector]
    Parser[Bookmark Parser]
    Generator[Metadata Generator]
    URLTracker[URL Tracker]
    VectorStore[Bookmark Vector Store]
    SearchModule[Semantic Search Module]
    ChromaDB[(ChromaDB)]
    JSONL[(JSONL Storage)]
    
    %% Flow
    User -->|generate| Server
    Server -->|Get Paths| Detector
    Detector -->|Browser Paths| Server
    
    subgraph Processing [Processing Loop]
        direction TB
        Server -->|Parse File| Parser
        Parser -->|Raw Bookmarks| Server
        Server -->|Check Processed| URLTracker
        URLTracker -->|Filter New| Server
        Server -->|Batch Process| Generator
        Generator -->|Fetch HTML| Generator
        Generator -->|Extract Metadata| Generator
        Generator -->|Enriched Bookmarks| Server
        Server -->|Store| VectorStore
        VectorStore -->|Generate Embeddings| SearchModule
        SearchModule -->|Store Vectors| ChromaDB
        Server -->|Write Metadata| JSONL
        Server -->|Track URLs| URLTracker
    end
    
    Server -->|JSON Result| User
    
    User -->|query| Server
    Server -->|Search| VectorStore
    VectorStore -->|Semantic Search| SearchModule
    SearchModule -->|Query Vectors| ChromaDB
    ChromaDB -->|Results| SearchModule
    SearchModule -->|Ranked Results| VectorStore
    VectorStore -->|Bookmarks| Server
    Server -->|JSON Results| User

    %% Styling
    style User fill:#ff9999,stroke:#333,stroke-width:2px
    style Server fill:#99ccff,stroke:#333,stroke-width:2px
    style Detector fill:#99ff99,stroke:#333,stroke-width:2px
    style Parser fill:#ffff99,stroke:#333,stroke-width:2px
    style Generator fill:#ffcc99,stroke:#333,stroke-width:2px
    style VectorStore fill:#cc99ff,stroke:#333,stroke-width:2px
    style SearchModule fill:#ff99cc,stroke:#333,stroke-width:2px
    style URLTracker fill:#99ffcc,stroke:#333,stroke-width:2px
    style ChromaDB fill:#9999ff,stroke:#333,stroke-width:2px
    style JSONL fill:#cccc99,stroke:#333,stroke-width:2px
    style Processing fill:#f9f9f9,stroke:#666,stroke-dasharray: 5 5

Structure

bookmark_geni_mcp/
├── config.yaml              # Server configuration file
├── mcp.json                 # MCP server configuration
├── pyproject.toml           # Project configuration
├── requirements.txt         # Python dependencies
├── servers/
│   └── bookmark_server.py   # MCP server implementation
├── scripts/
│   └── start_mcp_server.sh  # Bash start script
└── src/
    ├── browser_detector.py  # Browser path detection
    ├── bookmark_parser.py   # Bookmark file parsing
    ├── metadata_generator.py # HTML content and metadata extraction
    ├── bookmark_vector_store.py # Bookmark-specific vector store wrapper
    ├── metadata_storage.py  # JSONL file storage
    ├── config.py            # Configuration management
    └── search/              # Semantic search module
        ├── __init__.py
        ├── semantic_search.py
        ├── vector_store.py
        ├── embeddings.py
        └── config.py

bookmark_geni_mcp