Raindrop.io▌
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.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
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
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
- A Raindrop.io account
- A Raindrop.io API Access Token: https://app.raindrop.io/settings/integrations
Support
📋 Recent Enhancements (v2.3.9)
Smart Organization & Hierarchy
- AI Suggestions: New
get_suggestionstool provides organizational advice using Raindrop's API and MCP Sampling. - Collection Tree:
get_collection_treetool provides a hierarchical view with full breadcrumb paths. - Bulk Move: Added
moveoperation tobulk_edit_raindropsfor efficient library organization. - Pagination Support: Standardized
list_raindropsandbookmark_searchwith 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
resourcecontent 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}andmcp://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.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 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
4.6★★★★★39 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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