LlamaIndexFramework

Zotero MCP Server

Bring Reference Management
to LlamaIndex

Name: Zotero MCP Server
Price: 29 USD
Availability: InStock
Rating: 4.9 (1 reviews)
Author: Vinkius

Learn how to connect Zotero to LlamaIndex and start using 23 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.

GDPR Free for Subscribers

Create ItemsDelete ItemDelete ItemsGet CollectionGet DeletedGet ItemGet Item Type FieldsGet New Item TemplateGet TagList Collection ItemsList CollectionsList Item ChildrenList Item FieldsList Item TagsList Item TypesList ItemsList PublicationsList SubcollectionsList TagsList Top CollectionsList Top ItemsList Trash ItemsUpdate Item

Unlock for AI Agents

Ask AI about this MCP Server ChatGPT Claude Perplexity

Compatible with every major AI agent and IDE

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

+ other MCP clients

What is the Zotero MCP Server?

Connect your Zotero library to any AI agent and take full control of your research workflow through natural conversation.

What you can do

Collections & Organization — List top-level collections, subcollections, and specific collection details to navigate your library structure.
Item Management — Query all items, including notes and attachments, with support for advanced filtering by type, tag, or keyword.
Metadata Inspection — Fetch complete bibliographic data, creator information, and publication details for any specific item.
Tags & Publications — Access your personal publications and manage tags to categorize your research effectively.
Group Libraries — Seamlessly switch between your personal library and shared group libraries using specific IDs.

How it works

Subscribe to this server
Enter your Zotero API Key and User ID
Start managing your references from Claude, Cursor, or any MCP-compatible client

Who is this for?

Researchers & Academics — quickly find citations and organize papers without leaving your writing environment.
Students — manage reading lists and extract metadata for bibliographies through simple chat.
Knowledge Workers — centralize your document management and reference tracking within your AI-powered workspace.

Built-in capabilities (23)

create_items

Use get_new_item_template first to get the correct schema. Create new items in the Zotero library

delete_item

Delete a single item

delete_items

Delete multiple items (up to 50)

get_collection

Get a specific collection by key

get_deleted

Get deleted objects since a specific library version

get_item

Get a specific item by key

get_item_type_fields

List valid fields for a specific item type

get_new_item_template

Get a JSON template for creating a new item of a specific type

get_tag

Get tags matching a specific name

list_collection_items

List items in a specific collection

list_collections

List all collections in the Zotero library

list_item_children

List child items (notes, attachments) for a specific item

list_item_fields

List all available Zotero item fields

list_item_tags

List tags for a specific item

list_item_types

List all available Zotero item types

list_items

List items in the Zotero library

list_publications

List items in My Publications

list_subcollections

List subcollections of a specific collection

list_tags

List all tags in the library

list_top_collections

List top-level collections in the Zotero library

list_top_items

List top-level items in the Zotero library

list_trash_items

List items in the trash

update_item

Update an existing item (Partial Update / PATCH)

Why LlamaIndex?

LlamaIndex agents combine Zotero tool responses with indexed documents for comprehensive, grounded answers. Connect 23 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

—
Data-first architecture: LlamaIndex agents combine Zotero tool responses with indexed documents for comprehensive, grounded answers
—
Query pipeline framework lets you chain Zotero tool calls with transformations, filters, and re-rankers in a typed pipeline
—
Multi-source reasoning: agents can query Zotero, a vector store, and a SQL database in a single turn and synthesize results
—
Observability integrations show exactly what Zotero tools were called, what data was returned, and how it influenced the final answer

See it in action

Zotero in LlamaIndex

AI Agent→Vinkius

High Security·Kill Switch·Plug and Play

Why Vinkius

Zotero and 4,000+ other MCP servers. One platform. One governance layer.

Teams that connect Zotero to LlamaIndex through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.

4,000+MCP Servers ready

<40msCold start

60%Token savings

	Raw MCP	Vinkius
Server catalog	Find and host yourself	4,000+ managed
Infrastructure	Self-hosted	Sandboxed V8 isolates
Credential handling	Plaintext in config	Vault + runtime injection
Data loss prevention	None	Configurable DLP policies
Kill switch	None	Global instant shutdown
Financial circuit breakers	None	Per-server limits + alerts
Audit trail	None	Ed25519 signed logs
SIEM log streaming	None	Splunk, Datadog, Webhook
Honeytokens	None	Canary alerts on leak
Custom domains	Not applicable	DNS challenge verified
GDPR compliance	Manual effort	Automated purge + export

Unlock for AI Agents View step-by-step setup guide for LlamaIndex →

Enterprise Security

Why teams choose Vinkius for Zotero in LlamaIndex

The Zotero MCP Server runs on Vinkius-managed infrastructure inside AWS — a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts. All 23 tools execute in hardened sandboxes optimized for native MCP execution.

Your AI agents in LlamaIndex only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.

Unlock for AI Agents View full Zotero details →

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

The Vinkius Advantage

How Vinkius secures
Zotero for LlamaIndex

Every tool call from LlamaIndex to the Zotero MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.

< 40msCold start

Ed25519Signed audit chain

60%Token savings

FAQ

Frequently asked questions

Can I search for specific items in my library using keywords?

Yes! Use the list_items tool with the q parameter. It performs a quick search across titles and creators to find exactly what you need.

How do I view the subcollections nested inside a main collection?

Simply use the list_subcollections tool and provide the collection_key of the parent collection. The agent will return all nested folders.

Does this support shared group libraries or only my personal library?

It supports both! Most tools, like list_collections or list_items, accept an optional group_id. If provided, the agent will query that specific group library instead of your personal one.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Zotero tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.

Does LlamaIndex support async MCP calls?

Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

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