Raindrop MCP Server

Raindrop MCP server exists for a simple reason — assistants are far more useful when they can act on Raindrop directly instead of describing what you should do. MCP Server for Raindrop.io bookmark management.

What you get

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

What the assistant can call

Once Raindrop is connected, these are the calls the assistant has available:

• 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
• suggest_tags — Suggest relevant tags from bookmark metadata using AI-assisted analysis
• bulk_edit_raindrops — Bulk update, move, or remove bookmarks in a specific collection
• tag_manage — Rename, merge, or delete tags

Configuration and credentials

You will need 3 environment variables: RAINDROP_ACCESS_TOKEN, YOUR_RAINDROP_ACCESS_TOKEN, GITHUB_TOKEN. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.

• A Raindrop.io account - A Raindrop.io API Access Token: https://app.raindrop.io/settings/integrations

Setting it up

Being a remote server, there is no local install. You register the endpoint with your client, authorise it once, and the tools appear.

Choosing this one

Plenty of developer tooling servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Raindrop's toolset — diagnostics, collection_list, get_collection_tree and 11 more — is a fair guide to whether it matches your workflow. It is maintained by adeze; worth a glance at recent repository activity before you build anything load-bearing on it.

We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.

Before you rely on it

• Your data travels to the provider's service, so the usual questions apply about what you send and what they retain.
• With 14 tools registered it takes up a noticeable share of the context window; turn it off in projects that never touch Raindrop.
• Missing credentials fail quietly in some clients — if no tools show up, check the environment block first.
• MCP clients confirm each tool call by default. Leave that on until you have watched what the raindrop mcp server does with a few real requests.