productivity

Raindrop.io

adeze

by adeze

Easily manage and export bookmarks and favorites in Chrome with Raindrop.io. The top bookmark manager for collections, t

Integrates with Raindrop.io bookmarking service to provide direct access for managing collections, bookmarks, tags, highlights, and user data without leaving your conversation context.

github stars

128

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Requires Raindrop.io API tokenMultiple transport options

best for

  • / Knowledge workers managing research links
  • / Content creators organizing reference materials
  • / Anyone with large bookmark collections

capabilities

  • / Search through saved bookmarks
  • / Create new bookmarks
  • / Retrieve bookmark collections
  • / Access authenticated Raindrop.io account

what it does

Connect to your Raindrop.io bookmarks to search, add, and organize saved links directly from your AI assistant.

about

Raindrop.io is a community-built MCP server published by adeze that provides AI assistants with tools and capabilities via the Model Context Protocol. Easily manage and export bookmarks and favorites in Chrome with Raindrop.io. The top bookmark manager for collections, t It is categorized under productivity.

how to install

You can install Raindrop.io in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Raindrop.io is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

Raindrop.io MCP Server

smithery badge npm version Claude Desktop MCPB

Connect Raindrop.io to your AI assistant with a simple MCP server. Use it to organize, search, and manage bookmarks with natural language.

What it can do

  • Create, update, and delete collections and bookmarks
  • Search bookmarks by tags, domain, type, date, and more
  • Manage tags (list, rename, merge, delete)
  • Read highlights from bookmarks
  • Bulk edit bookmarks in a collection
  • Import/export bookmarks and manage trash

Tools

  • diagnostics - Server diagnostic information and library health metrics
  • collection_list - List all collections as a flat list
  • get_collection_tree - Hierarchical view of collections with full breadcrumb paths
  • collection_manage - Create, update, or delete collections
  • bookmark_search - Advanced search with filters, tags, and pagination
  • bookmark_manage - Create, update, or delete bookmarks
  • get_raindrop - Fetch a single bookmark by ID
  • list_raindrops - List bookmarks for a collection with pagination
  • get_suggestions - AI-powered organization advice (tags/collections) for a URL or bookmark
  • bulk_edit_raindrops - Bulk update, move, or remove bookmarks in a specific collection
  • tag_manage - Rename, merge, or delete tags
  • highlight_manage - Create, update, or delete highlights
  • library_audit - Scan library for broken links, duplicates, and untagged items
  • empty_trash - Permanently empty the trash (requires confirmation)
  • cleanup_collections - Remove empty collections (requires confirmation)

Install

Claude Desktop (MCPB)

Download the latest raindrop-mcp.mcpb from the GitHub Release and add it to Claude Desktop:

In Claude Desktop, add the bundle and set this environment variable:

  • RAINDROP_ACCESS_TOKEN (from your Raindrop.io integrations settings)

NPX (CLI)

Set your API token as an environment variable and run:

export RAINDROP_ACCESS_TOKEN=YOUR_RAINDROP_ACCESS_TOKEN
npx @adeze/raindrop-mcp

Manual MCP config (mcp.json)

Add this to your MCP client configuration:

{
  "servers": {
    "raindrop": {
      "type": "stdio",
      "command": "npx",
      "args": ["@adeze/raindrop-mcp@latest"],
      "env": {
        "RAINDROP_ACCESS_TOKEN": "YOUR_RAINDROP_ACCESS_TOKEN"
      }
    }
  }
}

Requirements

Support

📋 Recent Enhancements (v2.3.9)

Smart Organization & Hierarchy

  • AI Suggestions: New get_suggestions tool provides organizational advice using Raindrop's API and MCP Sampling.
  • Collection Tree: get_collection_tree tool provides a hierarchical view with full breadcrumb paths.
  • Bulk Move: Added move operation to bulk_edit_raindrops for efficient library organization.
  • Pagination Support: Standardized list_raindrops and bookmark_search with pagination for large libraries.

Safety & Quality

  • Confirmation Logic: Destructive tools (empty_trash, cleanup_collections) now require explicit confirmation.
  • Standardized Naming: All tools now use consistent snake_case naming conventions.
  • CI/CD Pipeline: Enhanced GitHub Actions with automated linting, type-checking, and cross-transport tests.
  • Code Quality: Established ESLint and Prettier configurations for maintainable development.

📋 Previous Enhancements (v2.3.3)

Advanced Cleanup & Library Audit

📋 Previous Enhancements (v2.3.2)

MCP Resource Links Implementation

  • Modern resource content following MCP SDK v1.25.3 best practices
  • Efficient data access: tools return lightweight links instead of full payloads
  • Better performance: clients fetch full bookmark/collection data only when needed
  • Seamless integration with dynamic resource system (mcp://raindrop/{id})

SDK & API Updates

  • Updated to MCP SDK v1.25.3
  • Modern tool registration with improved descriptions
  • Fixed API endpoints and path parameters
  • All core tools fully functional

Tool Optimization

  • Resource-efficient responses for bookmark/collection lists
  • Dynamic resource access via mcp://collection/{id} and mcp://raindrop/{id}
  • Better client UX with lighter list payloads
  • Full MCP compliance with official SDK patterns

Service Layer Improvements

  • Reduced code through extracted common helpers
  • Consistent error handling and response processing
  • Enhanced type safety with generic handlers
  • Centralized endpoint building

Testing Improvements

  • Stronger end-to-end coverage for MCP tool execution
  • Expanded integration tests for real-world client flows

MCP 2.0 Preparation (Bulk Ops)

  • Laying groundwork for MCP 2.0 bulk-operation workflows and tooling

OAuth (Coming Soon)

  • OAuth-based auth flow to simplify setup without manual tokens

Note

Apologies to anyone affected by the last couple of builds. Thank you for the patience and reports.

License

This project is licensed under the MIT License - see the LICENSE file for details.

FAQ

What is the Raindrop.io MCP server?
Raindrop.io is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Raindrop.io?
This profile displays 39 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.639 reviews
  • Nikhil Gill· Dec 28, 2024

    Raindrop.io is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Ava Johnson· Dec 16, 2024

    Raindrop.io is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Yuki Mensah· Dec 16, 2024

    Raindrop.io reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Chaitanya Patil· Dec 8, 2024

    I recommend Raindrop.io for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Piyush G· Nov 27, 2024

    Strong directory entry: Raindrop.io surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Isabella Zhang· Nov 23, 2024

    We evaluated Raindrop.io against two servers with overlapping tools; this profile had the clearer scope statement.

  • Ren Harris· Nov 19, 2024

    Raindrop.io reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Anika Thompson· Nov 7, 2024

    We wired Raindrop.io into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Anika Garcia· Nov 7, 2024

    Raindrop.io is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Ava Garcia· Oct 26, 2024

    According to our notes, Raindrop.io benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

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