bookmark-lens 0.0.1

Local-first MCP server for intelligent bookmark management with semantic search

Bookmark Lens

A local-first MCP server for intelligent bookmark management with semantic search.

Save, search, and organize your bookmarks using AI-powered semantic search. Works completely offline (no LLM required for core features).

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Features

• 🔍 Semantic Search - Find bookmarks by meaning, not just keywords
• 📝 Rich Metadata - Automatic extraction of titles, descriptions, and content
• 🤖 Smart Mode - LLM-powered summaries, auto-tags, and topic classification (optional)
• 🏷️ Smart Tagging - Manual tags + auto-generated tags (Smart Mode)
• 📊 Topic Classification - Automatic categorization (Smart Mode)
• 📅 Date Filtering - Search by time ranges (natural language supported via LLM)
• 🌐 Domain Filtering - Filter by website
• 💾 Local-First - All data stored locally (DuckDB + LanceDB)
• 🤖 MCP Native - Works with Claude Desktop and other MCP clients
• ⚡ Fast - Local embeddings with sentence-transformers

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Quick Setup

Claude Desktop

1. Open your Claude Desktop config file:

   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%\Claude\claude_desktop_config.json

2. Add bookmark-lens to the mcpServers section:

{
  "mcpServers": {
    "bookmark-lens": {
      "command": "uvx",
      "args": ["bookmark-lens"]
    }
  }
}

3. Restart Claude Desktop

That's it! No installation, no setup, no configuration needed.

Other MCP Clients

For other MCP-compatible clients, use:

uvx bookmark-lens

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Example Conversations

Save a bookmark:

You: Save https://docs.anthropic.com/en/docs/build-with-claude/model-context-protocol 
     with note "MCP documentation for building servers"

Claude: [Saves bookmark with automatic title/content extraction]

Search bookmarks:

You: Find bookmarks about AI agents from last week

Claude: [Converts "last week" to date range, searches semantically]

Search with filters:

You: Show me GitHub bookmarks about React

Claude: [Searches with domain filter and semantic query]

Update bookmarks:

You: Add tag "tutorial" to that bookmark

Claude: [Updates tags while preserving existing ones]

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Architecture

bookmark-lens/
├── src/bookmark_lens/
│   ├── server.py              # MCP server (stdio transport)
│   ├── config.py              # Configuration management
│   ├── database/
│   │   ├── duckdb_client.py   # Relational data (bookmarks, tags)
│   │   └── lancedb_client.py  # Vector embeddings
│   ├── models/
│   │   └── bookmark.py        # Pydantic models
│   └── services/
│       ├── content_fetcher.py # Web page fetching
│       ├── embedding_service.py # Text → vectors
│       ├── bookmark_service.py # Orchestration
│       └── search_service.py  # Hybrid search
├── data/                      # Local databases (gitignored)
└── tests/
    └── manual_test.py         # End-to-end testing

Technology Stack

• MCP SDK - Model Context Protocol for AI integration
• DuckDB - Relational database (bookmarks, metadata, tags)
• LanceDB - Vector database (embeddings for semantic search)
• sentence-transformers - Local embedding model (all-MiniLM-L6-v2)
• readability-lxml - Content extraction from web pages
• Pydantic - Data validation and serialization

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MCP Tools

save_bookmark

Save a URL with optional note and tags.

Parameters:

• url (required): URL to bookmark
• note (optional): Context or reason for saving
• tags (optional): List of tags

Example:

{
  "url": "https://example.com/article",
  "note": "Great explanation of embeddings",
  "tags": ["ai", "ml", "tutorial"]
}

search_bookmarks

Search bookmarks semantically with optional filters.

Parameters:

• query (required): What to search for
• domain (optional): Filter by domain (e.g., "github.com")
• tags (optional): Filter by tags
• from_date (optional): ISO 8601 date string
• to_date (optional): ISO 8601 date string
• limit (optional): Max results (default: 10)

Example:

{
  "query": "machine learning tutorials",
  "domain": "github.com",
  "tags": ["python"],
  "from_date": "2024-11-07T00:00:00Z",
  "limit": 5
}

get_bookmark

Get full details about a bookmark by ID.

Parameters:

• id (required): Bookmark ID

update_bookmark

Update note and/or tags for a bookmark.

Parameters:

• id (required): Bookmark ID
• note (optional): New note
• tags (optional): Tags to add/replace
• tag_mode (optional): "replace" or "append" (default: "replace")

delete_bookmark

Delete a bookmark and all its associated data.

Parameters:

• id (required): Bookmark ID

Example:

{
  "id": "bkm_abc123"
}

list_tags

List all tags with their usage counts.

Parameters: None

Example Response:

{
  "success": true,
  "count": 5,
  "tags": [
    {"tag": "ai", "count": 20},
    {"tag": "python", "count": 15},
    {"tag": "tutorial", "count": 8}
  ]
}

get_bookmark_stats

Get statistics about your bookmark collection with optional filters.

Parameters:

• stat_type (optional): Type of statistics
  • "total" - Total count (default)
  • "by_domain" - Breakdown by domain
  • "by_topic" - Breakdown by topic
  • "by_tag" - Breakdown by tag
  • "by_date" - Activity over time
• domain (optional): Filter by domain
• topic (optional): Filter by topic
• tags (optional): Filter by tags
• from_date (optional): Filter after date (ISO 8601)
• to_date (optional): Filter before date (ISO 8601)
• limit (optional): For breakdown stats, top N results (default: 10)

Examples:

Total bookmarks:

{
  "stat_type": "total"
}

Bookmarks saved this week:

{
  "stat_type": "total",
  "from_date": "2024-11-07T00:00:00Z"
}

Top domains:

{
  "stat_type": "by_domain",
  "limit": 5
}

AI bookmarks by domain:

{
  "stat_type": "by_domain",
  "topic": "AI"
}

---

Configuration

All configuration is via environment variables (.env file):

# Database paths
BOOKMARK_LENS_DB_PATH=./data/bookmark_lens.db
LANCE_DB_PATH=./data/embeddings.lance

# Embedding model
EMBEDDING_MODEL_NAME=all-MiniLM-L6-v2
EMBEDDING_DIMENSION=384

# Content fetching
BOOKMARK_LENS_FETCH_TIMEOUT=30
BOOKMARK_LENS_USER_AGENT=bookmark-lens/0.1.0
MAX_CONTENT_LENGTH=50000

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Smart Mode (LLM Enhancements)

Enable Smart Mode to get automatic summaries, tags, and topic classification for your bookmarks.

Setup

1. Choose an LLM model (see LiteLLM providers)
2. Get an API key from your provider
3. Add to .env:

   LLM_MODEL=claude-3-haiku-20240307
   LLM_API_KEY=your-api-key-here
   

4. Restart the server

Recommended Models

• claude-3-haiku-20240307 - Fast, cheap, good quality (Anthropic) [Recommended]
• gpt-4o-mini - Fast, cheap (OpenAI)
• gpt-4o - Better quality, more expensive (OpenAI)
• claude-3-5-sonnet-20241022 - Best quality (Anthropic)

See LiteLLM documentation for 100+ supported models.

What Smart Mode Adds

• Auto-summaries: Short (1-2 sentences) and long (1 paragraph) summaries
• Auto-tags: 3-5 relevant tags automatically generated
• Topic classification: High-level category (AI, Cloud, Programming, Data, Security, DevOps, Design, Business, Science, Other)
• Better search: Summaries and topics included in embeddings for improved relevance
• Markdown extraction: Full content extracted as Markdown (preserves structure)

Cost Estimate

With claude-3-haiku-20240307: ~$0.0005 per bookmark (very cheap!)

Performance

• Core Mode (no LLM): Fast saves, only title/description extracted
• Smart Mode (with LLM): Slower saves (~5-10s), full content + enhancements

Note: Smart Mode is completely optional. All core features work without any LLM configuration.

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Embedding Models

Default: all-MiniLM-L6-v2 (384 dimensions, fast, good quality)

Alternatives:

• all-mpnet-base-v2 (768 dimensions, better quality, slower)
• paraphrase-multilingual-MiniLM-L12-v2 (384 dimensions, multilingual)

Change in .env:

EMBEDDING_MODEL_NAME=all-mpnet-base-v2
EMBEDDING_DIMENSION=768

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How It Works

Saving a Bookmark

1. Fetch - Downloads the web page
2. Extract - Pulls out title, description, main content (Markdown in Smart Mode)
3. Enhance - Generates summaries, tags, topic (Smart Mode only)
4. Embed - Converts text to vector using local model
5. Store - Saves to DuckDB (metadata) and LanceDB (vector)

Searching Bookmarks

1. Embed Query - Converts search text to vector
2. Vector Search - Finds similar bookmarks (LanceDB)
3. Filter - Applies domain/tag/date filters (DuckDB)
4. Rank - Sorts by similarity score
5. Return - Top N results with relevance scores

Natural Language Dates

The LLM (via the bookmark_search_guide prompt) converts natural language to ISO dates:

• "yesterday" → 2024-11-13T00:00:00Z
• "last week" → 2024-11-07T00:00:00Z
• "last month" → 2024-10-14T00:00:00Z

The server only accepts ISO 8601 format - the LLM does the conversion.

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Development

Want to contribute? See CONTRIBUTING.md for setup instructions.

Running Tests

# Clone the repository
git clone https://github.com/yourusername/bookmark-lens.git
cd bookmark-lens

# Install in development mode
pip install -e ".[dev]"

# Run tests
python tests/test_simple.py

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Troubleshooting

"Model not found" error

The first run downloads the embedding model (~80MB). This is normal and happens once.

"Database locked" error

Close any other processes using the database. DuckDB doesn't support concurrent writes.

Search returns no results

• Check if bookmarks were saved successfully
• Try a broader query
• Verify embedding model loaded correctly

Slow first search

The embedding model loads on first use. Subsequent searches are fast.

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Roadmap

Phase 2 (Smart Mode - Future)

• LLM-powered summaries
• Auto-tagging
• Topic classification
• Query expansion

Future Features

• Browser history import
• Browser extension
• Export/import bookmarks
• Bookmark collections
• Sharing capabilities

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License

MIT License - see LICENSE file for details.

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Contributing

Contributions welcome! Please:

1. Check TASKS.md for current status
2. Follow existing code style (minimal, focused implementations)
3. Add tests for new features
4. Update documentation

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Credits

Built with:

• MCP SDK by Anthropic
• DuckDB - Fast analytical database
• LanceDB - Vector database
• sentence-transformers - Embedding models
• readability-lxml - Content extraction