Raindrop.io MCP Server

@sachin-philip

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基本信息

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其他

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stdio

作者

sachin-philip

关于 Raindrop.io MCP Server

An MCP server for Raindrop.io

#raindrop #mcp-server

Raindrop.io MCP Server 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"
      ]
    }
  }
}

Raindrop.io MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

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

常见问题

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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