🌧️ Raindrop.io MCP Server

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

@sachin-philip

代码仓库主页

关于 🌧️ Raindrop.io MCP Server

An MCP server for Raindrop.io

#raindrop #mcp-server

基本信息

分类

其他

运行时

python

传输方式

stdio

发布者

sachin-philip

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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.