Raindrop.io MCP Server

The Raindrop.io MCP Server bridges the gap between AI assistants and a user’s personal bookmarking ecosystem. By exposing Raindrop.io’s RESTful API through the Model Context Protocol, it allows language models to perform rich data operations—creating, reading, updating, and deleting bookmarks and collections—without leaving the conversational context. This capability solves a common pain point for developers: integrating external knowledge bases into AI workflows while keeping data handling secure and declarative.

At its core, the server implements a comprehensive set of MCP resources that mirror Raindrop.io’s functionality. It offers CRUD operations for collections and bookmarks, advanced search filters (tags, domains, types, dates), tag management tools (list, rename, merge, delete), and even retrieval of text highlights embedded in bookmarks. Users can reorder collections, collapse or expand nested structures, merge empty folders, and manage file uploads directly to Raindrop.io. The server also supports reminders, import/export workflows, and trash cleanup—all exposed as intuitive MCP tools with clear parameter schemas.

For developers building AI-powered assistants, this translates into a plug‑in that can fetch relevant resources on demand. For example, a productivity bot could ask the user to “find all bookmarks tagged #research,” and the MCP server would return a structured list of matching items. A knowledge‑base assistant could retrieve highlights from a bookmarked article to answer user queries about its content. The streaming support (Server‑Sent Events) ensures that updates—such as a new bookmark or tag change—are pushed to the AI client in real time, enabling dynamic, stateful interactions.

The server’s design emphasizes developer ergonomics. It follows MCP best practices with modern patterns, providing nine core tools that are easy to discover and invoke. Parameter validation is handled by Zod schemas, guaranteeing type safety across the protocol boundary. Built in TypeScript and leveraging Axios for HTTP requests, it offers maintainable code while remaining lightweight enough to run in a single command via npx.

In practice, the Raindrop.io MCP Server is ideal for use cases that require contextual knowledge retrieval, content curation automation, or collaborative bookmarking workflows. Whether you’re building a research assistant that aggregates sources, a meeting notes bot that pulls relevant links from a shared collection, or an automated workflow that organizes new bookmarks by tag and reminder, this server provides the reliable, protocol‑compliant bridge needed to bring Raindrop.io data into AI‑driven applications.