rdrop for Raindrop.io MCP server

rdrop for Raindrop.io

Find, review and organize your saved links. Keep your tags. Bring your own AI.

Turn a growing Raindrop.io library into something easier to use. Search saved links, inspect collections, find cleanup opportunities and review suggestions before making changes. Use it from your terminal or let your favorite agent do the reading and planning.

A Raindrop.io CLI, MCP server and reviewed bookmark organizer from Machineyearn, built as an independent integration.

• Your organization stays yours. Start with the tags and collections you already created; preserve existing tags when applying a plan.
• Changes you can inspect. See exact bookmarks, proposed tags and destinations, then dry-run the plan before applying it.
• AI that fits your workflow. Connect Codex or Claude, use a hosted tool-capable model, or run local Qwen and Laya.
• A useful CLI without AI. Search, edit, export, manage highlights and collections, and build shell pipelines.

Get started

1. Install

brew install KingPsychopath/tap/rdrop
# Or Node 22+, with optional dependencies enabled:
npm install --global @machineyearn/raindrop-cli@0.2.1
rdrop version

Prefer no global install? Use npx --yes @machineyearn/raindrop-cli@0.2.1 COMMAND. Native downloads cover macOS, Linux and Windows on Intel/AMD and ARM. Builds and checksums.

2. Connect Raindrop

Create a token in your Raindrop integration settings. Add it to the private file ~/.config/rdrop/env:

RAINDROP_TOKEN=your_token_here

Give the directory permissions 700 and the file 600, then run rdrop doctor. Keep the token out of chat and Git. Setup and troubleshooting.

3. Ask your agent

Copy this into Codex, Claude or your preferred coding agent. It can help with setup too:

Set up https://github.com/KingPsychopath/raindrop-cli so you can help
manage my Raindrop library. Use its plugin/MCP when available, otherwise
use the CLI. Check installation and authentication; have me add the token
privately. Use the existing model and configured services first.

Start with a cleanup report and a small sample. Use my existing tags and
collections. Choose useful tools for search, content, classification and
planning; connect extra model providers, Executor or ACP only if needed.
Show the exact proposed changes and a dry-run. Preserve my tags and notes,
flag uncertain suggestions, and apply only the exact plan I approve.

Connect an agent, or start in your shell with rdrop collections --tree, rdrop search 'type:article #go' --json and rdrop cleanup report --json.

Choose your workflow

Shortened. The full README is on GitHub.