UniArticles MCP Server
Unifies academic literature retrieval from multiple sources like Scopus, ArXiv, PubMed, and Google Scholar into a single standardized API for LLM agents.
README
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) and literature APIs (PubMed, Google 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, author search, quota check.
- ScienceDirect: Article search, metadata search, full-text retrieval (requires entitlement).
- ArXiv: Search papers, search by ID, list recent papers, download PDF.
- Paperscraper APIs: PubMed search and Google Scholar title search.
- Google Scholar Stability Notice: Google Scholar access may be unstable or temporarily unavailable; this part is experimental/test-only.
- 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.
Note: Even without the above API key, 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"
}
}
}
}
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"
}
}
}
}
📖 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: https://github.com/CherryHQ/cherry-studio/issues/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-mcp
Using 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 uniarticles
Configuration
Create a .env file in the project root:
SCOPUS_API_KEY=your_elsevier_api_key
ARXIV_DOWNLOAD_DIR=./arxiv_downloads
Project Structure
src/
└── uniarticles/
├── server.py # MCP Server entry point
└── sources/ # Data source modules
├── arxiv.py
├── paperscraper.py
├── scopus.py
└── ...
tests/ # Integration and verification tests
pyproject.toml # Project metadata and dependencies
Testing
Run automated integration tests:
python -m unittest discover tests
Verify MCP protocol handshake:
python tests/verify_server.py
Available Tools
Scopus
search_scopus(query, count, sort, view): Search for documents.get_abstract_details(eid, view): Get detailed abstract information.get_author_profile(author_id, view): Get author profile information.search_authors(query, count, view): Search Scopus authors.get_quota_status(): Check Elsevier API quota (via Scopus endpoint).
ScienceDirect
search_sciencedirect(query, count, start, view): Search ScienceDirect records.get_article_metadata(query, count, start, view): Search article metadata.retrieve_article(identifier, identifier_type, view): Retrieve full-text article record.
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.
Paperscraper
search_pubmed_papers(query, max_results): Search papers from PubMed.search_scholar_papers(title): Search paper metadata from Google Scholar by title (experimental; may fail when Google Scholar is unstable).
🤝 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.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。