# Raindrop > Store and retrieve bookmarks in Raindrop.io using the Raindrop MCP server. Use when saving research sources, PDFs, articles, or any URL encountered during work. Also use when searching or browsing existing bookmarks by tag, text, or collection. - Skill: `danieldekay/raindrop` (Agent Skill) - Install (CLI): `npx skillmds@latest add danieldekay/raindrop` - Raw SKILL.md: https://api.skillmd.com/api/skills/danieldekay/raindrop/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: danieldekay (https://skillmd.com/u/danieldekay) - Updated: 2026-09-10 - Page: https://skillmd.com/skills/danieldekay/raindrop --- # Raindrop Bookmarking Skill ## Overview Use the `raindrop/*` MCP tools to save, tag, and retrieve bookmarks in the user's Raindrop.io account. Every useful URL discovered during research, every PDF downloaded, and every tool or documentation page referenced should be bookmarked with structured metadata. ## MCP Setup The Raindrop MCP is an **official hosted HTTP server** provided by Raindrop.io. It requires a **Raindrop Pro** account. **Endpoint:** `https://api.raindrop.io/rest/v2/ai/mcp` **Transport:** Streamable HTTP **Docs:** **Client setup guide (Claude, VS Code, Cursor, ChatGPT):** ### Configuring in VS Code (mcp.json) ```json { "servers": { "raindrop": { "type": "http", "url": "https://api.raindrop.io/rest/v2/ai/mcp" } } } ``` Authentication is handled via **OAuth 2.1** (browser prompt on first use) or a **Bearer token** from the Raindrop.io REST API. > If the `mcp_raindrop_*` tools are not available, direct the user to the setup guide at . ## MCP Tools Available | Tool | Purpose | |------|---------| | `mcp_raindrop_create_bookmark` | Save a single URL with title, excerpt, tags, collection | | `mcp_raindrop_update_bookmark` | Update excerpt/tags on an existing bookmark (e.g. after processing) | | `mcp_raindrop_list_bookmarks` | List recent bookmarks, optionally by collection | | `mcp_raindrop_search_bookmarks` | Search by text query, tags, and/or collection | | `mcp_raindrop_search_bookmarks_by_text` | Quick full-text search | | `mcp_raindrop_search_bookmarks_by_tags` | Filter by tag(s) | | `mcp_raindrop_bulk_create_bookmarks` | Save a list of raw URLs in one call | | `mcp_raindrop_scan_and_add_links` | Scan markdown files/dirs for URLs and bookmark them | | `mcp_raindrop_analyze_research_links` | Analyze files for link patterns — use before bulk-saving | | `mcp_raindrop_delete_bookmark` | Remove a bookmark by ID | ## Collections Configuration Collection IDs are personal and differ per account. This skill uses a local config file so the collections reference is not hardcoded here. ### Config file location ``` ~/.config/raindrop-skill/collections.json ``` ### First use — create the config On first use (or when `collection_id` is needed), check whether the config exists: ```bash ls ~/.config/raindrop-skill/collections.json ``` If missing, **create it** by: 1. Fetching the user's collections via `mcp_raindrop_list_bookmarks` or by calling the Raindrop REST API: ``` GET https://api.raindrop.io/rest/v1/collections Authorization: Bearer ``` 2. Writing the result to `~/.config/raindrop-skill/collections.json` in this format: ```json { "_schema": "raindrop-skill-collections-v1", "_instructions": "Map human-readable names to Raindrop collection IDs. Add 'description' to help the agent pick the right collection.", "collections": [ { "id": 0, "name": "Unsorted", "description": "Default inbox — use when no better collection fits" } ] } ``` 1. Ask the user to review and enrich the file with `description` fields so the agent can pick the right collection automatically. ### Using the config When a `collection_id` is needed: 1. Read `~/.config/raindrop-skill/collections.json`. 2. Match the content domain/topic to the best collection by `description`. 3. Fall back to the `Unsorted` collection (id `0` or omit `collection_id`) if no match is found. > For research sessions: prefer the most specific collection. Omitting `collection_id` lands the bookmark in "All Bookmarks". ## Standard Excerpt Format Always write excerpts in this structured format so they are searchable and self-documenting: ``` [CATEGORY] YYYY-MM-DD | . . ZK: [[note-id]]. Session: . ``` **Examples:** ``` [research][methodology] 2026-02-28 | Pacheco-Vega AIC reading method. Core digestion protocol for literature-reviewer agent. ZK: [[20260228T...md]]. Session: .github/agents/literature-reviewer.agent.md [tools][mcp] 2026-02-28 | FastMCP v3 stdio transport for VS Code. run_stdio_async() pattern. Session: code/src/raindrop/mcp_server/ [tango][community] 2026-02-15 | Study on milonga attendance decline in European cities. Key: COVID accelerated pre-existing trends. ZK: [[20260215T...md]]. Session: content/blog/01/ ``` ## Standard Tag Taxonomy Apply tags from multiple levels — always at least one from each applicable level: ### Level 1 — Domain (pick one) `research` · `tools` · `tango` · `writing` · `tech` · `home` · `health` · `work` · `personal` ### Level 2 — Content Type `methodology` · `tutorial` · `reference` · `paper` · `pdf` · `video` · `thread` · `tool` · `api` · `mcp` · `book` ### Level 3 — Processing State `inbox` — not yet read `lit-review` — being processed via AIC `aic-processed` — AIC note created, ZK link in excerpt `tier-1` through `tier-4` — deep-research tier classification ### Level 4 — Topic Tags Free-form, specific: `pacheco-vega` · `fastmcp` · `zettelkasten` · `clean-architecture` · `raindrop` · `limesurvey` · etc. ## Workflows ### Save a source during research ``` 1. Read ~/.config/raindrop-skill/collections.json → pick best collection_id 2. mcp_raindrop_create_bookmark( url=, title=, excerpt="[domain][type] YYYY-MM-DD | <summary>. Session: <path>", tags=["research", "inbox", "<topic>"], collection_id=<id from config> ) 3. After AIC note created → mcp_raindrop_update_bookmark( bookmark_id=<id>, excerpt="... ZK: [[note-id]]. AIC: complete.", tags=[..., "aic-processed"] # replace "inbox" with "aic-processed" ) ``` ### Save a downloaded PDF ``` 1. Read ~/.config/raindrop-skill/collections.json → pick best collection_id 2. mcp_raindrop_create_bookmark( url=<original-pdf-url>, title=<paper-title>, excerpt="[research][pdf] YYYY-MM-DD | <abstract-summary>. PDF: notes/papers/YYYY-Author-keyword.pdf. Session: <path>", tags=["research", "pdf", "lit-review", "<topic>"], collection_id=<id from config> ) ``` ### Bulk-save URLs from markdown files ``` 1. mcp_raindrop_analyze_research_links( file_paths=["/path/to/notes"], file_patterns=["*.md"] ) # Review output — check which URLs are new/relevant 2. mcp_raindrop_bulk_create_bookmarks( urls=["https://...", "https://..."], default_tags=["research", "inbox"], ) ``` ### Retrieve bookmarks by topic ``` # By tag mcp_raindrop_search_bookmarks_by_tags(tags=["aic-processed"]) # By text mcp_raindrop_search_bookmarks_by_text(text_query="<keyword>") # By collection (read id from config) mcp_raindrop_list_bookmarks(collection_id=<id from config>) # Combined mcp_raindrop_search_bookmarks(query="<topic>", tags=["research"], collection_id=<id from config>) ``` ## Rules 1. **Bookmark on first encounter** — don't wait until the end of a session. 2. **Always include an excerpt** — a URL without context is useless later. 3. **Use `inbox` tag** on first save; replace with `aic-processed` after digestion. 4. **Pick a collection** — avoid leaving everything in "All Bookmarks". 5. **Update after AIC** — add ZK note ID to excerpt when a Zettelkasten note is created. 6. **Deduplicate first** — run `search_bookmarks_by_text` before creating to avoid duplicates.