Zotero MCP Server for LlamaIndexGive LlamaIndex instant access to 23 tools to Create Items, Delete Item, Delete Items, and more

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

GDPR Free for Subscribers

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Zotero as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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The Zotero MCP Server for LlamaIndex is a standout in the Productivity category — giving your AI agent 23 tools to work with, ready to go from day one.

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python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Zotero. "
            "You have 23 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Zotero?"
    )
    print(response)

asyncio.run(main())

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About Zotero MCP Server

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

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.

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.

The Zotero MCP Server exposes 23 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 23 Zotero tools available for LlamaIndex

When LlamaIndex connects to Zotero through Vinkius, your AI agent gets direct access to every tool listed below — spanning reference-management, citation-tools, academic-research, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

create

Create items on Zotero

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

delete

Delete item on Zotero

Delete a single item

delete

Delete items on Zotero

Delete multiple items (up to 50)

get

Get collection on Zotero

Get a specific collection by key

get

Get deleted on Zotero

Get deleted objects since a specific library version

get

Get item on Zotero

Get a specific item by key

get

Get item type fields on Zotero

List valid fields for a specific item type

get

Get new item template on Zotero

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

get

Get tag on Zotero

Get tags matching a specific name

list

List collection items on Zotero

List items in a specific collection

list

List collections on Zotero

List all collections in the Zotero library

list

List item children on Zotero

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

list

List item fields on Zotero

List all available Zotero item fields

list

List item tags on Zotero

List tags for a specific item

list

List item types on Zotero

List all available Zotero item types

list

List items on Zotero

List items in the Zotero library

list

List publications on Zotero

List items in My Publications

list

List subcollections on Zotero

List subcollections of a specific collection

list

List tags on Zotero

List all tags in the library

list

List top collections on Zotero

List top-level collections in the Zotero library

list

List top items on Zotero

List top-level items in the Zotero library

list

List trash items on Zotero

List items in the trash

update

Update item on Zotero

Update an existing item (Partial Update / PATCH)

Connect Zotero to LlamaIndex via MCP

Follow these steps to wire Zotero into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 23 tools from Zotero

Why Use LlamaIndex with the Zotero MCP Server

LlamaIndex provides unique advantages when paired with Zotero through the Model Context Protocol.

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

Zotero + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Zotero MCP Server delivers measurable value.

Hybrid search: combine Zotero real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Zotero to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Zotero for fresh data

Analytical workflows: chain Zotero queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for Zotero in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Zotero immediately.

"List all my top-level collections in Zotero."

"Search for items in my library with the tag 'artificial-intelligence'."

"Get the complete bibliographic details for item key ABCD1234."

Troubleshooting Zotero MCP Server with LlamaIndex

Common issues when connecting Zotero to LlamaIndex through Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Zotero + LlamaIndex FAQ

Common questions about integrating Zotero MCP Server with LlamaIndex.

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.