by kujenga
Integrate Zotero with top citations tools. Search and manage scholarly references using software Zotero and zoterobib fo
Connects to your Zotero research library to browse collections, search papers, and retrieve citation details. Works through Zotero's API to access your existing research database.
Zotero is a community-built MCP server published by kujenga that provides AI assistants with tools and capabilities via the Model Context Protocol. Integrate Zotero with top citations tools. Search and manage scholarly references using software Zotero and zoterobib fo It is categorized under productivity.
You can install Zotero in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
Zotero is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
I recommend Zotero for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
We wired Zotero into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Useful MCP listing: Zotero is the kind of server we cite when onboarding engineers to host + tool permissions.
Zotero is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
We evaluated Zotero against two servers with overlapping tools; this profile had the clearer scope statement.
We wired Zotero into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Strong directory entry: Zotero surfaces stars and publisher context so we could sanity-check maintenance before adopting.
Zotero is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
I recommend Zotero for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Zotero reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
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This project is a python server that implements the Model Context Protocol (MCP) for Zotero, giving you access to your Zotero library within AI assistants. It is intended to implement a small but maximally useful set of interactions with Zotero for use with MCP clients.
<a href="https://glama.ai/mcp/servers/jknz38ntu4"> <img width="380" height="200" src="https://glama.ai/mcp/servers/jknz38ntu4/badge" alt="Zotero Server MCP server" /> </a>This MCP server provides the following tools:
zotero_search_items: Search for items in your Zotero library using a text queryzotero_item_metadata: Get detailed metadata information about a specific Zotero itemzotero_item_fulltext: Get the full text of a specific Zotero item (i.e. PDF contents)These can be discovered and accessed through any MCP client or through the MCP Inspector.
Each tool returns formatted text containing relevant information from your Zotero items, and AI assistants such as Claude can use them sequentially, searching for items then retrieving their metadata or text content.
This server can either run against either a local API offered by the Zotero desktop application) or through the Zotero Web API. The local API can be a bit more responsive, but requires that the Zotero app be running on the same computer with the API enabled. To enable the local API, do the following steps:
[!IMPORTANT] For access to the
/fulltextendpoint on the local API which allows retrieving the full content of items in your library, you'll need to install a Zotero Beta Build (as of 2025-03-30). Once 7.1 is released this will no longer be the case. See https://github.com/zotero/zotero/pull/5004 for more information. If you do not want to do this, use the Web API instead.
To use the Zotero Web API, you'll need to create an API key and find your Library ID (usually your User ID) in your Zotero account settings here: https://www.zotero.org/settings/keys
These are the available configuration options:
ZOTERO_LOCAL=true: Use the local Zotero API (default: false, see note below)ZOTERO_API_KEY: Your Zotero API key (not required for the local API)ZOTERO_LIBRARY_ID: Your Zotero library ID (your user ID for user libraries, not required for the local API)ZOTERO_LIBRARY_TYPE: The type of library (user or group, default: user)uvx with Local Zotero APITo use this with Claude Desktop and a direct python install with uvx, add the following to the mcpServers configuration:
{
"mcpServers": {
"zotero": {
"command": "uvx",
"args": ["--upgrade", "zotero-mcp"],
"env": {
"ZOTERO_LOCAL": "true",
"ZOTERO_API_KEY": "",
"ZOTERO_LIBRARY_ID": ""
}
}
}
}
The --upgrade flag is optional and will pull the latest version when new ones are available. If you don't have uvx installed you can use pipx run instead, or clone this repository locally and use the instructions in Development below.
If you want to run this MCP server in a Docker container, you can use the following configuration, inserting your API key and library ID:
{
"mcpServers": {
"zotero": {
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"-e", "ZOTERO_API_KEY=PLACEHOLDER",
"-e", "ZOTERO_LIBRARY_ID=PLACEHOLDER",
"ghcr.io/kujenga/zotero-mcp:main"
],
}
}
}
To update to a newer version, run docker pull ghcr.io/kujenga/zotero-mcp:main. It is also possible to use the docker-based installation to talk to the local Zotero API, but you'll need to modify the above command to ensure that there is network connectivity to the Zotero application's local API interface.
Information on making changes and contributing to the project.
uv sync.env file in the project root with the environment variables aboveStart the MCP Inspector for local development:
npx @modelcontextprotocol/inspector uv run zotero-mcp
To test the local repository against Claude Desktop, run echo $PWD/.venv/bin/zotero-mcp in your shell within this directory, then set the following within your Claude Desktop configuration
{
"mcpServers": {
"zotero": {
"command": "/path/to/zotero-mcp/.venv/bin/zotero-mcp"
"env": {
// Whatever configuration is desired.
}
}
}
}
To run the test suite:
uv run pytest
Build the container image with this command:
docker build . -t zotero-mcp:local
To test the container with the MCP inspector, run the following command:
npx @modelcontextprotocol/inspector \
-e ZOTERO_API_KEY=$ZOTERO_API_KEY \
-e ZOTERO_LIBRARY_ID=$ZOTERO_LIBRARY_ID \
docker run --rm -i \
--env ZOTERO_API_KEY \
--env ZOTERO_LIBRARY_ID \
zotero-mcp:local
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.