zotero-mcp-server

zotero-mcp-server

An MCP server for the Zotero Web API v3 that lets you search, read, and write items, collections, tags, and notes in a Zotero library, supporting literature-review workflows.

Category
Visit Server

README

zotero-mcp-server

An MCP (Model Context Protocol) server for the Zotero Web API v3 — search, read, and write items, collections, tags, and notes in a Zotero library.

Built for use with Claude (or any MCP client), aimed at literature-review workflows: pulling existing references into a "registro maestro," filing newly-found sources back into Zotero, tagging items by theoretical line, and attaching notes.

Tools

Tool Read/Write Description
zotero_search_items Read Quick-search items by text, type, tag, or collection
zotero_get_item Read Full metadata for one item, optionally with a formatted citation
zotero_get_item_children Read Notes/attachments of an item
zotero_list_collections Read List collections, optionally scoped to a parent
zotero_list_tags Read List tags in use, with item counts
zotero_create_item Write Create a new bibliographic item
zotero_add_note Write Attach a note to an existing item
zotero_update_item_tags Write Add/remove tags on an item (version-checked)
zotero_create_collection Write Create a new collection/subcollection

This is a focused set covering the core research workflow, not full API coverage. Not included (would need to be added if you need them): saved searches, file/attachment uploads, full item updates beyond tags, deletion, group-membership management.

1. Get a Zotero API key

  1. Go to https://www.zotero.org/settings/keys and create a new private key.
  2. Grant it read/write access to the library you want to use (your personal library, and/or specific groups).
  3. Note your numeric user ID, shown on the same page — this is not your username.
    • For a group library, the group ID is the number in the group's URL, or from GET https://api.zotero.org/users/<userID>/groups.

2. Configure environment variables

Variable Required Description
ZOTERO_API_KEY Yes The key generated above
ZOTERO_LIBRARY_TYPE Yes user or group
ZOTERO_LIBRARY_ID Yes Numeric user ID or group ID
ZOTERO_API_BASE_URL No Override for the local Zotero desktop API (http://localhost:23119/api/) instead of https://api.zotero.org
TRANSPORT No stdio (default) or http
PORT No Port for TRANSPORT=http (default: 3000)

Never commit these values or hardcode them in source — set them as environment variables or secrets in whatever you use to run the server.

3. Build

npm install
npm run build

4. Run

Locally, over stdio (for local MCP clients, e.g. Claude Desktop's local server config):

ZOTERO_API_KEY=... ZOTERO_LIBRARY_TYPE=user ZOTERO_LIBRARY_ID=... node dist/index.js

As a remote server, over Streamable HTTP (needed to add it as a custom connector in claude.ai):

TRANSPORT=http PORT=3000 \
ZOTERO_API_KEY=... ZOTERO_LIBRARY_TYPE=user ZOTERO_LIBRARY_ID=... \
node dist/index.js

This exposes POST /mcp (the MCP endpoint) and GET /health (a plain liveness check). Deploy it somewhere reachable over HTTPS (Render, Fly.io, Railway, a small VM behind a reverse proxy with TLS, etc.) — claude.ai's custom connector setup needs a public https:// URL, not localhost.

A Dockerfile is included, so any of the platforms below can build and run it with no extra configuration beyond setting environment variables.

Deploying to a public HTTPS endpoint

Pick one. Render is the easiest if you don't mind pushing to GitHub; Fly.io is the easiest if you'd rather stay in a terminal.

Option A — Render (dashboard, needs a GitHub repo)

  1. Push this project (this whole folder) to a new GitHub repository.
  2. In the Render dashboard, click New → Web Service and connect that repository.
  3. Render will detect the Dockerfile automatically — leave Environment as Docker. Leave build/start commands blank (the Dockerfile handles both).
  4. Under Environment Variables, add:
    • ZOTERO_API_KEY
    • ZOTERO_LIBRARY_TYPE (user or group)
    • ZOTERO_LIBRARY_ID
    • (TRANSPORT=http is already set inside the Dockerfile — no need to add it.)
  5. Click Create Web Service. Render builds the image and deploys it; this takes a few minutes the first time.
  6. Once live, Render shows a URL like https://zotero-mcp-server.onrender.com. Your MCP endpoint is https://zotero-mcp-server.onrender.com/mcp.
  7. Sanity check: curl https://zotero-mcp-server.onrender.com/health should return {"status":"ok"}.

Free-tier note: Render's free web services sleep after inactivity and take a few seconds to wake on the next request — fine for testing, worth upgrading if you'll use this daily.

Option B — Fly.io (CLI, no GitHub needed)

  1. Install the CLI: curl -L https://fly.io/install.sh | sh (or see fly.io/docs/flyctl).
  2. fly auth login
  3. From inside the zotero-mcp-server folder: fly launch
    • It detects the Dockerfile and proposes an app name and region — accept or edit.
    • Say no to adding a Postgres/Redis database (not needed).
    • Say no to deploying immediately if it asks — set secrets first (next step).
  4. Set your credentials as secrets (never as plain fly.toml values):
    fly secrets set ZOTERO_API_KEY=your_key ZOTERO_LIBRARY_TYPE=user ZOTERO_LIBRARY_ID=your_id
    
  5. Open the generated fly.toml and confirm internal_port = 3000 under [http_service] (it should be auto-detected from the Dockerfile's EXPOSE 3000; fix it manually if not).
  6. Deploy: fly deploy
  7. Your MCP endpoint is https://<your-app-name>.fly.dev/mcp. Check https://<your-app-name>.fly.dev/health.

Option C — Railway (CLI or dashboard)

  1. Install the CLI (npm install -g @railway/cli) or use the Railway dashboard connected to a GitHub repo — same idea as Render.
  2. CLI path: railway login, then from the project folder railway init and railway up. Railway detects the Dockerfile automatically.
  3. Set env vars: railway variables set ZOTERO_API_KEY=... ZOTERO_LIBRARY_TYPE=user ZOTERO_LIBRARY_ID=... (or via the dashboard's Variables tab).
  4. Railway services aren't public by default — go to Settings → Networking → Generate Domain to get a public https://your-app.up.railway.app URL.
  5. MCP endpoint: https://your-app.up.railway.app/mcp.

Option D — Self-hosted VM (full control, more steps)

  1. Provision a small Ubuntu 22.04+ VM (DigitalOcean, Linode, a spare EC2 instance, etc.) and point a DNS A record at its IP, e.g. zotero-mcp.yourdomain.com.
  2. SSH in and install Node 20 and Caddy (Caddy handles HTTPS automatically via Let's Encrypt — much less setup than nginx + certbot):
    curl -fsSL https://deb.nodesource.com/setup_20.x | sudo bash -
    sudo apt-get install -y nodejs
    sudo apt-get install -y debian-keyring debian-archive-keyring apt-transport-https
    curl -1sLf 'https://dl.cloudsmith.io/public/caddy/stable/gpg.key' | sudo gpg --dearmor -o /usr/share/keyrings/caddy-stable-archive-keyring.gpg
    curl -1sLf 'https://dl.cloudsmith.io/public/caddy/stable/debian.deb.txt' | sudo tee /etc/apt/sources.list.d/caddy-stable.list
    sudo apt-get update && sudo apt-get install -y caddy
    
  3. Copy the project to the VM (scp the zip, then unzip) and build:
    cd zotero-mcp-server && npm install && npm run build
    
  4. Create /etc/systemd/system/zotero-mcp.service:
    [Unit]
    Description=Zotero MCP server
    After=network.target
    
    [Service]
    Environment=TRANSPORT=http
    Environment=PORT=3000
    Environment=ZOTERO_API_KEY=your_key
    Environment=ZOTERO_LIBRARY_TYPE=user
    Environment=ZOTERO_LIBRARY_ID=your_id
    ExecStart=/usr/bin/node /home/youruser/zotero-mcp-server/dist/index.js
    Restart=always
    User=youruser
    
    [Install]
    WantedBy=multi-user.target
    
    Then: sudo systemctl daemon-reload && sudo systemctl enable --now zotero-mcp
  5. Point Caddy at it — edit /etc/caddy/Caddyfile:
    zotero-mcp.yourdomain.com {
        reverse_proxy localhost:3000
    }
    
    Then: sudo systemctl reload caddy. Caddy fetches a TLS certificate automatically on first request.
  6. Check https://zotero-mcp.yourdomain.com/health. MCP endpoint: https://zotero-mcp.yourdomain.com/mcp.

5. Connect it to Claude

Once deployed and reachable over HTTPS:

  1. In claude.ai, go to Settings → Connectors → Add custom connector.
  2. Enter the deployed URL, e.g. https://your-deployment.example.com/mcp.
  3. Claude will discover the 9 tools above automatically.

For Claude Desktop with a local stdio server instead, add an entry to its MCP server config pointing at node /absolute/path/to/dist/index.js, with the environment variables from step 2 set in that config.

Testing

npx @modelcontextprotocol/inspector node dist/index.js

This opens a local UI to call each tool by hand before wiring it up to Claude.

Notes on the Zotero API this server relies on

  • Auth: Zotero-API-Key header, per request.
  • Versioning for writes: zotero_update_item_tags reads the item's current version before patching, and sends it via If-Unmodified-Since-Version — if the item changed elsewhere in the meantime, Zotero returns 412 and the tool reports it clearly instead of silently overwriting.
  • Rate limits: Zotero may return 429 with a Retry-After header, or a Backoff header on any response. This server surfaces both as actionable error text; it does not currently auto-retry.
  • Item creation fetches the field template for the requested itemType from GET /items/new first, so only valid fields for that type are sent.

Official API docs: https://www.zotero.org/support/dev/web_api/v3/

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured
E2B

E2B

Using MCP to run code via e2b.

Official
Featured