zotero-comfort
High-level Zotero MCP integration with smart research workflows, enabling tasks like building reading lists, adding papers with duplicate check, and exporting bibliographies.
README
Zotero Comfort
High-level Zotero MCP integration with smart research workflows.
Overview
Zotero Comfort provides two layers of functionality:
A) Proxy Layer - Direct re-exposure of 54yyyu/zotero-mcp tools with a clean Python API.
B) Smart Workflows - High-level orchestrations for common research tasks.
Installation
pip install zotero-comfort
Or with Docker:
docker build -t zotero-comfort .
docker run -e ZOTERO_API_KEY=xxx -e ZOTERO_LIBRARY_ID=123 zotero-comfort
Configuration
Set environment variables:
export ZOTERO_API_KEY="your-api-key"
export ZOTERO_LIBRARY_ID="your-library-id"
export ZOTERO_LIBRARY_TYPE="group" # or "user"
Usage
As Python Library
from zotero_comfort import ZoteroProxy, ZoteroWorkflows
# Proxy layer - direct tool access
proxy = ZoteroProxy()
papers = proxy.search_papers("FHIR interoperability", limit=20)
metadata = proxy.get_metadata("ABC12345")
# Smart workflows - high-level operations
workflows = ZoteroWorkflows()
reading_list = workflows.build_reading_list("clinical NLP", max_papers=15)
result = workflows.smart_add_paper("10.1234/example.2024")
bibtex = workflows.export_bibliography(collection_name="FHIR")
As MCP Server
Add to your Claude configuration:
{
"mcpServers": {
"zotero-comfort": {
"command": "zotero-comfort",
"env": {
"ZOTERO_API_KEY": "your-key",
"ZOTERO_LIBRARY_ID": "your-library-id"
}
}
}
}
Available Tools
Proxy Layer (A)
| Tool | Description |
|---|---|
zotero_search |
Search papers by keyword |
zotero_get_metadata |
Get paper details |
zotero_list_collections |
List all collections |
zotero_get_collection_items |
Get items in collection |
zotero_get_fulltext |
Get paper full text |
zotero_semantic_search |
AI-powered semantic search |
Smart Workflows (B)
| Tool | Description |
|---|---|
build_reading_list |
Create curated topic reading list |
smart_add_paper |
Add paper with duplicate check |
export_bibliography |
Export as BibTeX |
find_related_papers |
Find semantically similar papers |
Development
# Install dev dependencies
pip install -e ".[dev]"
# Run tests
pytest
# Lint
ruff check src/
License
MIT
Recommended Servers
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.
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.
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.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
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.
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.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
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.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.