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Raindrop.io Bookmarks MCP Server

Knowledge & MemoryTypeScript
Interact with Raindrop.io bookmarks from your AI assistant
Available Tools

create-bookmark

Creates a new bookmark in your Raindrop.io account

urltitletagscollection

search-bookmarks

Searches through your Raindrop.io bookmarks

querytags

The Raindrop.io Bookmarks integration allows AI assistants to seamlessly interact with your Raindrop.io bookmarks collection. With this integration, you can create new bookmarks, search through your existing collection, and filter results by tags - all through natural language conversations with your AI. This integration bridges the gap between your AI assistant and your knowledge management workflow, making it easier to save valuable resources and retrieve information when needed. Whether you're researching a topic, organizing web resources, or building a knowledge base, this integration streamlines the process of managing your digital bookmarks.

Overview

The Raindrop.io Bookmarks integration connects your AI assistant to your Raindrop.io account, allowing you to manage your bookmarks through conversation. This integration is particularly useful for researchers, knowledge workers, and anyone who regularly saves and organizes web content.

Prerequisites

Before setting up this integration, you'll need:

  1. A Raindrop.io account
  2. A Raindrop.io API token
  3. Node.js 16 or higher installed on your system

Installation

Option 1: Install via Smithery (Recommended)

The easiest way to install the Raindrop.io integration is through Smithery:

npx -y @smithery/cli install @hiromitsusasaki/raindrop-io-mcp-server --client claude

This command automatically configures the integration for Claude Desktop.

Option 2: Manual Installation

If you prefer to install manually:

  1. Clone the repository:
git clone https://github.com/hiromitsusasaki/raindrop-io-mcp-server
cd raindrop-io-mcp-server
  1. Install dependencies:
npm install
  1. Build the project:
npm run build
  1. Configure your AI client to use the integration (see Configuration section below)

Getting a Raindrop.io API Token

  1. Go to the Raindrop.io Developer Portal
  2. Create a new app
  3. Copy your API token

Configuration

For Claude Desktop

  1. Open the Claude Desktop configuration file:

    • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
    • Windows: %APPDATA%\Claude\claude_desktop_config.json
  2. Add the following configuration (replace your_access_token_here with your actual Raindrop.io API token):

{
  "mcpServers": {
    "raindrop": {
      "command": "node",
      "args": ["PATH_TO_BUILD/index.js"],
      "env": {
        "RAINDROP_TOKEN": "your_access_token_here"
      }
    }
  }
}

Replace PATH_TO_BUILD with the actual path to the built index.js file.

  1. Save the file and restart Claude Desktop

For Other AI Clients

For other AI clients that support the Model Context Protocol, consult your client's documentation for how to add MCP servers. You'll need to provide:

  • The path to the built index.js file
  • Your Raindrop.io API token as an environment variable

Usage Examples

Once installed, you can interact with your Raindrop.io bookmarks through natural language:

  • "Save this article to my Raindrop.io: https://example.com/article"
  • "Search my Raindrop.io bookmarks for machine learning resources"
  • "Find bookmarks with the tags 'research' and 'AI'"

Troubleshooting

If you encounter issues:

  1. Verify your API token is correct
  2. Check that the path to the index.js file is correct in your configuration
  3. Ensure Node.js 16+ is installed
  4. Restart your AI client after making configuration changes

Security Considerations

  • Store your API token securely
  • Be aware that your AI assistant will have access to create and search your bookmarks
  • Consider the permissions you grant to the integration

Additional Resources

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A persistent memory system using a local knowledge graph

MemoryMesh
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A knowledge graph server for structured memory persistence in AI models

Cognee
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Knowledge management and retrieval system with code graph capabilities

About Model Context Protocol

Model Context Protocol (MCP) allows AI models to access external tools and services, extending their capabilities beyond their training data.

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