thinktraveller/UniArticles_MCPserver
UniArticles-An MCP (Model Context Protocol) tool that queries and curates new research papers from Scopus, arXiv and more using APIs/official libraries. 亿文通——一个MCP(模型上下文协议)工具,通过 API/官方库聚合检索并整理新文献。
UniArticles MCP Server
Overview
UniArticles(亿文通) is a unified academic literature retrieval server implementing the Model Context Protocol (MCP). Integrates multiple scholarly databases (Scopus, ArXiv, Semantic Scholar) into a single, standardized API for LLM agents (like Claude).
Features
- Unified Interface: Single search structure for all sources.
- Multi-Source Support:
- Scopus: Search, abstract details, author profiles, quota check.
- ArXiv: Search papers, search by ID, list recent papers, download PDF.
- Semantic Scholar: Search papers.
- Standardized Returns: Consistent JSON structure (
ok,source,query,count,items,error). - Secure Configuration: API keys managed via environment variables.
⚠️ API Key Requirements
This server integrates multiple data sources, and some advanced features require API keys:
-
Elsevier API (Scopus database, Required):
- How to get: Apply at Elsevier Developer Portal.
- Restriction: Your institution must have a subscription to Elsevier's services; otherwise, you cannot use related functions even with an API Key.
- Clarification: Scopus is an Elsevier database. The
SCOPUS_API_KEYconfigured here is an Elsevier API key and may also be used for other Elsevier API services allowed by your subscription and key scope.
-
Semantic Scholar (Recommended):
- How to get: Apply at Semantic Scholar API Key Form.
- Restriction: Using an institutional email is recommended. Without an API Key, rate limits and results will be severely restricted.
Note: Even without the above API keys, you can still use other functions normally.
Installation & Usage
Method 1: Direct Integration with LLM Clients (Recommended)
Suitable for Cherry Studio, LM Studio, Claude Desktop, Trae, etc.
This project is published on PyPI, so you can configure it directly without downloading the full source code.
Since these LLM clients are already configured with Python and uv environments, no additional downloads are required.
Simply add the following configuration to your client's MCP settings (e.g., claude_desktop_config.json):
{
"mcpServers": {
"uniarticles-mcp-server": {
"command": "uvx",
"args": [
"--refresh",
"uniarticles-mcp"
],
"env": {
"SCOPUS_API_KEY": "your_elsevier_api_key_here",
"SEMANTIC_SCHOLAR_API_KEY": "your_semantic_scholar_api_key_here"
}
}
}
}If you do not want to force refresh the cache package every time you restart, then instead add the following content: (but this will cause you to need to manually update the package when the package is updated)
{
"mcpServers": {
"uniarticles-mcp-server": {
"command": "uvx",
"args": [
"uniarticles-mcp"
],
"env": {
"SCOPUS_API_KEY": "your_elsevier_api_key_here",
"SEMANTIC_SCHOLAR_API_KEY": "your_semantic_scholar_api_key_here"
}
}
}
}📖 Troubleshooting? See: Step-by-Step Configuration Guide
If you encounter MCP error -32000: Connection closed when starting the service, please find the solution in the related Cherry Studio issue: CherryHQ/cherry-studio#3264
Method 2: Local Installation (Advanced)
Requires Python 3.10+ and uv (recommended) or pip.
Useful for developers or those who want to modify the source code.
Using uv:
# Clone the repository
git clone https://github.com/your-username/UniArticles_MCPserver.git
cd UniArticles_MCPserver
# Sync dependencies and run
uv sync
uv run uniarticles-mcpUsing pip:
# Clone and setup venv
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
# Install dependencies
pip install -e .
# Run
python -m uniarticlesConfiguration
Create a .env file in the project root:
SCOPUS_API_KEY=your_elsevier_api_key
SEMANTIC_SCHOLAR_API_KEY=your_semantic_scholar_api_key
ARXIV_DOWNLOAD_DIR=./arxiv_downloadsProject Structure
src/
└── uniarticles/
├── server.py # MCP Server entry point
└── sources/ # Data source modules
├── arxiv.py
├── scopus.py
├── semanticscholar.py
└── ...
tests/ # Integration and verification tests
pyproject.toml # Project metadata and dependencies
Testing
Run automated integration tests:
python -m unittest discover testsVerify MCP protocol handshake:
python tests/verify_server.pyAvailable Tools
Scopus
search_scopus(query, count, sort): Search for documents.get_abstract_details(eid): Get detailed abstract information.get_author_profile(author_id): Get author profile information.get_quota_status(): Check Elsevier API quota (via Scopus endpoint).
ArXiv
search_arxiv(query, max_results): Search papers.list_papers(max_results): List recent papers.read_paper(paper_id): Get paper metadata.download_paper(paper_id, filename, output_dir): Download PDF.
Semantic Scholar
search_semantic_scholar(query, limit): Search papers.
🤝 Call for Contributions
Due to the author's background in Chemistry, I am less familiar with databases and API developments in other research fields. I warmly welcome contributions and Pull Requests (PRs) from the community to add more data sources!
⚖️ License & Acknowledgments
License
AGPL-3.0 License with Commercial Restriction
This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0).
🔴 Commercial Use Restriction:
Commercial use of this software is permitted ONLY with explicit written authorization from the author.
Special Acknowledgments
-
ScopusMCP:
ScopusMCP is the first literature retrieval MCP tool the author successfully developed, but initially it was quite bloated and difficult to port.Thanks to my roommate (https://github.com/qwe4559999) for the suggestion to use pypi and uv for packaging. -
ArxivMCPserver:
Integrated directly from the ArxivMCPserver project.
Special Declaration
This project uses AI-generated content.