PaperMCP 智能学术论文检索系统

欢迎使用 PaperMCP 智能学术论文检索系统！这是一个基于 Model Context Protocol (MCP) 的高级学术论文搜索服务器，专为研究员和教授设计。通过 OpenAlex API 和智能算法，为AI助手提供精准的学术文献检索能力，大幅提升科研效率。

🌟 Features

📚 Comprehensive Paper Search

Search academic papers with flexible filtering options:

• Keyword Search - Find papers by title, abstract, or full-text content
• Country Filter - Limit results to papers from specific countries (CN, US, GB, etc.)
• Year Filter - Search papers from specific publication years
• Result Limit - Control the number of results (up to 50 papers)
• Sort Options - Sort by citation count, publication date, or relevance
• Open Access Filter - Find only freely accessible papers

📊 Rich Paper Information

Get comprehensive details for each paper:

• Basic Info - Title, authors, publication year, document type
• Abstract - Full abstract text with intelligent reconstruction from inverted index
• Publication Details - Journal/venue, DOI, URLs
• Citation Data - Citation count and related works
• Institutional Info - Author affiliations and institutions
• Subject Classification - Topics, subfields, fields, and domains
• Open Access Status - OA status and APC (Article Processing Charge) information

🔍 Advanced Filtering

• Institution-based Filtering - Find papers from specific countries' institutions
• Temporal Filtering - Search within specific publication years
• Access-based Filtering - Filter by open access availability
• Quality Indicators - Sort by citation impact or publication date

🤖 MCP Integration

Seamless integration with MCP-compatible clients (like Claude) for intelligent academic research

📊 Supported Search Parameters

The PaperMCP server supports the following search parameters:

📈 Data Sources

This server uses the OpenAlex API, which provides:

• 260M+ papers from across all disciplines
• Real-time updates with new publications
• Comprehensive metadata including citations, authors, institutions
• Open access information and APC data
• Subject classification at multiple levels
• Institution and country data for geographic analysis

🔮 Future Plans

Future enhancements may include:

1. Author Search - Find papers by specific authors
2. Institution Search - Search within specific institutions
3. Journal/Venue Filtering - Filter by publication venue
4. Citation Network Analysis - Explore citation relationships
5. Concept-based Search - Search by research concepts and topics
6. Export Functionality - Export results in various formats (BibTeX, etc.)

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

👨‍💻 Author

• Name: Xingyu_Chen
• Email: guangxiangdebizi@gmail.com
• GitHub: guangxiangdebizi

🙏 Acknowledgments

This project uses the OpenAlex API, a free and open catalog of scholarly papers, authors, institutions, and more. Special thanks to the OpenAlex team for providing this invaluable resource to the research community.

🚦 Requirements

Before getting started, please ensure you have:

1. Node.js and npm:

   • Requires Node.js version >= 18
   • Download and install from nodejs.org

2. Email Address:

   • Provide a valid email address for OpenAlex API access
   • OpenAlex requires an email for rate limiting and contact purposes
   • No API key needed - OpenAlex is free to use!

🛠️ Installation & Setup

Install via Smithery (Recommended)

If you're using Claude Desktop, you can quickly install via Smithery:

npx -y @smithery/cli install @guangxiangdebizi/paper-mcp --client claude

Manual Installation

1. Get the code:

   Copy

   git clone https://github.com/guangxiangdebizi/PaperMCP.git
   cd PaperMCP

2. Install dependencies:

   Copy

   npm install

3. Configure Email Address:

   • Create a .env file in the project root directory
   • Add the following content:
     Copy

     OPENALEX_EMAIL=your_email@example.com

   • Or set it directly in the src/config.ts file

4. Build the project:

   Copy

   npm run build

🚀 Running the Server

There are two ways to start the server:

Method 1: Using stdio mode (Direct run)

node build/index.js

Method 2: Using Supergateway (Recommended for development)

npx supergateway --stdio "node build/index.js" --port 3100

📝 Configuring MCP Clients

To use this server in Claude or other MCP clients, you need the following configuration:

Claude Configuration

Add the following to Claude's configuration file:

{
  "mcpServers": {
    "paper-search-server": {
      "url": "http://localhost:3100/sse", // If using Supergateway
      "type": "sse",
      "disabled": false,
      "autoApprove": [
        "paper_search"
      ]
    }
  }
}

If using stdio mode directly (without Supergateway), configure as follows:

{
  "mcpServers": {
    "paper-search-server": {
      "command": "C:/path/to/PaperMCP/build/index.js", // Modify to actual path
      "type": "stdio",
      "disabled": false,
      "autoApprove": [
        "paper_search"
      ]
    }
  }
}

💡 Usage Examples

Here are some example queries using the PaperMCP server:

1. Basic Paper Search

You can ask Claude:

General Search:

"Search for papers about machine learning published in 2024"

Country-specific Search:

"Find papers about artificial intelligence from Chinese institutions in 2023"

Author/Institution Focus:

"Search for papers about LLM from US universities in the last 2 years"

2. Advanced Filtering

Citation-based Search:

"Find the most-cited papers about deep learning from 2022, limited to 20 results"

Open Access Papers:

"Search for open access papers about natural language processing from 2024"

Specific Year Range:

"Find papers about computer vision published in 2023, sorted by citation count"

3. Research-focused Queries

Literature Review:

"Help me find recent papers about transformer architectures for my literature review"

Trend Analysis:

"Search for papers about quantum computing from different countries to analyze research trends"

Interdisciplinary Research:

"Find papers that combine AI and biology, focusing on recent publications"

4. Complex Research Queries

Comparative Analysis:

"Compare recent AI research output between China and the US by finding papers from both countries in 2024"

Field Evolution:

"Show me how research in reinforcement learning has evolved by finding papers from 2020-2024"

Open Science Focus:

"Find highly-cited open access papers in machine learning to understand accessible research trends"

This will use the paper_search tool to retrieve comprehensive academic paper information.