🌧️ Raindrop.io MCP Server

Name: 🌧️ Raindrop.io MCP Server
Author: sachin-philip

@sachin-philip

リポジトリホームページ

🌧️ Raindrop.io MCP Server について

An MCP server for Raindrop.io

#raindrop #mcp-server

基本情報

カテゴリ

その他

ランタイム

python

トランスポート

stdio

公開者

sachin-philip

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "raindrop.io-mcp": {
      "command": "uv",
      "args": [
        "activate",
        "&&",
        "uv",
        "install"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is 🌧️ Raindrop.io MCP Server?

This server integrates the Raindrop.io bookmark manager with LLM applications, allowing you to add, search, and organize bookmarks programmatically. It is intended for users who want to manage their Raindrop.io bookmarks directly from AI tools like Claude or Cursor.

How to use 🌧️ Raindrop.io MCP Server?

Install Python 3.11 and the UV package manager. Get an API token from the Raindrop.io Developer Portal, then add the MCP server configuration (command uv, arguments pointing to the project directory and raindrop.py, and the RAINDROP_TOKEN environment variable) to your LLM app’s MCP config. Restart your app. Alternatively, install automatically via Smithery.

Key features of 🌧️ Raindrop.io MCP Server

Add a bookmark with tags, description, and collection
Retrieve the latest bookmarks
Search bookmarks by tag
Search bookmarks by keyword or text

Use cases of 🌧️ Raindrop.io MCP Server

Save a link from an LLM conversation directly to Raindrop.io
Quickly fetch and review your most recent bookmarks
Organize bookmarks by searching specific tags
Find a bookmark by describing its content or keywords

FAQ from 🌧️ Raindrop.io MCP Server

What are the prerequisites?

You need Python 3.11 and the UV package manager installed.

How do I get a Raindrop.io API token?

Go to the Raindrop.io Developer Portal (https://developer.raindrop.io/v1/authentication/token) and generate a token.

How do I configure the MCP server?

Add a block to your MCP configuration with command: "uv", args pointing to the directory and raindrop.py, and env containing RAINDROP_TOKEN with your token. Restart your LLM application.

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