Paper Search MCP (Node.js)

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A Node.js Model Context Protocol (MCP) server for searching and downloading academic papers from multiple sources, including arXiv, Web of Science, PubMed, Google Scholar, Sci-Hub, ScienceDirect, Springer, Wiley, Scopus, and 13 academic platforms in total.

✨ Key Features

• 🌍 13 Academic Platforms: arXiv, Web of Science, PubMed, Google Scholar, bioRxiv, medRxiv, Semantic Scholar, IACR ePrint, Sci-Hub, ScienceDirect, Springer Nature, Wiley, Scopus
• 🔗 MCP Protocol Integration: Seamless integration with Claude Desktop and other AI assistants
• 📊 Unified Data Model: Standardized paper format across all platforms
• ⚡ High-Performance Search: Concurrent search with intelligent rate limiting
• 🛡️ Type Safety: Complete TypeScript support
• 🎯 Academic Papers First: Smart filtering prioritizing academic papers over books
• 🔄 Smart Error Handling: Platform fallback and auto-retry mechanisms

📚 Supported Platforms

✅ Supported | ❌ Not supported | 🟡 Optional | ✅* Open Access only

🚀 Quick Start

System Requirements

• Node.js >= 18.0.0
• npm or yarn

Installation

# Clone repository
git clone https://github.com/your-username/paper-search-mcp-nodejs.git
cd paper-search-mcp-nodejs

# Install dependencies
npm install

# Copy environment template
cp .env.example .env

Configuration

1. Get Web of Science API Key

   • Visit Clarivate Developer Portal
   • Register and apply for Web of Science API access
   • Add API key to .env file

2. Get PubMed API Key (Optional)

   • Without API key: Free usage, 3 requests/second limit
   • With API key: 10 requests/second, more stable service
   • Get key: See NCBI API Keys

3. Configure Environment Variables

   # Edit .env file
   WOS_API_KEY=your_actual_api_key_here
   WOS_API_VERSION=v1
   
   # PubMed API key (optional, recommended for better performance)
   PUBMED_API_KEY=your_ncbi_api_key_here
   
   # Semantic Scholar API key (optional, increases rate limits)
   SEMANTIC_SCHOLAR_API_KEY=your_semantic_scholar_api_key
   
   # Elsevier API key (required for ScienceDirect and Scopus)
   ELSEVIER_API_KEY=your_elsevier_api_key
   
   # Springer Nature API keys (required for Springer)
   SPRINGER_API_KEY=your_springer_api_key  # For Metadata API v2
   # Optional: Separate key for OpenAccess API (if different from main key)
   SPRINGER_OPENACCESS_API_KEY=your_openaccess_api_key
   
   # Wiley TDM token (required for Wiley)
   WILEY_TDM_TOKEN=your_wiley_tdm_token
   

Build and Run

Method 1: NPX (Recommended for MCP)

# Direct run with npx (most common MCP deployment)
npx -y paper-search-mcp-nodejs

# Or install globally
npm install -g paper-search-mcp-nodejs
paper-search-mcp

Method 2: Local Development

# Build TypeScript code
npm run build

# Start server
npm start

# Or run in development mode
npm run dev

MCP Server Configuration

Add the following configuration to your Claude Desktop config file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

NPX Configuration (Recommended)

{
  "mcpServers": {
    "paper-search-nodejs": {
      "command": "npx",
      "args": ["-y", "paper-search-mcp-nodejs"],
      "env": {
        "WOS_API_KEY": "your_web_of_science_api_key"
      }
    }
  }
}

Local Installation Configuration

{
  "mcpServers": {
    "paper_search_nodejs": {
      "command": "node",
      "args": ["/path/to/paper-search-mcp-nodejs/dist/server.js"],
      "env": {
        "WOS_API_KEY": "your_web_of_science_api_key"
      }
    }
  }
}

🛠️ MCP Tools

search_papers

Search academic papers across multiple platforms

// Random platform selection (default behavior)
search_papers({
  query: "machine learning",
  platform: "all",      // Randomly selects one platform for efficiency
  maxResults: 10,
  year: "2023",
  sortBy: "date"
})

// Search specific platform
search_papers({
  query: "quantum computing",
  platform: "webofscience",  // Target specific platform
  maxResults: 5
})

Platform Selection Behavior:

• platform: "all" - Randomly selects one platform for efficient, focused results
• Specific platform - Searches only that platform
• Available platforms: arxiv, webofscience/wos, pubmed, biorxiv, medrxiv, semantic, iacr, googlescholar/scholar, scihub, sciencedirect, springer, wiley, scopus

search_arxiv

Search arXiv preprints specifically

search_arxiv({
  query: "transformer neural networks",
  maxResults: 10,
  category: "cs.AI",
  author: "Attention"
})

search_webofscience

Search Web of Science database specifically

search_webofscience({
  query: "CRISPR gene editing",
  maxResults: 15,
  year: "2022",
  journal: "Nature"
})

search_pubmed

Search PubMed/MEDLINE biomedical literature database

search_pubmed({
  query: "COVID-19 vaccine efficacy",
  maxResults: 20,
  year: "2023",
  author: "Smith",
  journal: "New England Journal of Medicine",
  publicationType: ["Journal Article", "Clinical Trial"]
})

search_google_scholar

Search Google Scholar academic database

search_google_scholar({
  query: "machine learning",
  maxResults: 10,
  yearLow: 2020,
  yearHigh: 2023,
  author: "Bengio"
})

search_biorxiv / search_medrxiv

Search biology and medical preprints

search_biorxiv({
  query: "CRISPR",
  maxResults: 15,
  days: 30
})

search_semantic_scholar

Search Semantic Scholar AI semantic database

search_semantic_scholar({
  query: "deep learning",
  maxResults: 10,
  fieldsOfStudy: ["Computer Science"],
  year: "2023"
})

search_iacr

Search IACR ePrint cryptography archive

search_iacr({
  query: "zero knowledge proof",
  maxResults: 5,
  fetchDetails: true
})

search_scihub

Search and download papers from Sci-Hub using DOI or paper URL

search_scihub({
  doiOrUrl: "10.1038/nature12373",
  downloadPdf: true,
  savePath: "./downloads"
})

check_scihub_mirrors

Check health status of Sci-Hub mirror sites

check_scihub_mirrors({
  forceCheck: true  // Force fresh health check
})

download_paper

Download paper PDF files

download_paper({
  paperId: "2106.12345",  // or DOI for Sci-Hub
  platform: "arxiv",      // or "scihub" for Sci-Hub downloads
  savePath: "./downloads"
})

get_paper_by_doi

Get paper information by DOI

get_paper_by_doi({
  doi: "10.1038/s41586-023-12345-6",
  platform: "all"
})

get_platform_status

Check platform status and API keys

get_platform_status({})

📊 Data Model

All platform paper data is converted to a unified format:

interface Paper {
  paperId: string;           // Unique identifier
  title: string;            // Paper title
  authors: string[];        // Author list
  abstract: string;         // Abstract
  doi: string;             // DOI
  publishedDate: Date;     // Publication date
  pdfUrl: string;          // PDF link
  url: string;             // Paper page URL
  source: string;          // Source platform
  citationCount?: number;   // Citation count
  journal?: string;         // Journal name
  year?: number;           // Publication year
  categories?: string[];    // Subject categories
  keywords?: string[];      // Keywords
  // ... more fields
}

🔧 Development

Project Structure

src/
├── models/
│   └── Paper.ts              # Paper data model
├── platforms/
│   ├── PaperSource.ts        # Abstract base class
│   ├── ArxivSearcher.ts      # arXiv searcher
│   ├── WebOfScienceSearcher.ts # Web of Science searcher
│   ├── PubMedSearcher.ts     # PubMed searcher
│   ├── GoogleScholarSearcher.ts # Google Scholar searcher
│   ├── BioRxivSearcher.ts    # bioRxiv/medRxiv searcher
|   ├── SemanticScholarSearcher.ts # Semantic Scholar searcher
|   ├── IACRSearcher.ts       # IACR ePrint searcher
|   ├── SciHubSearcher.ts     # Sci-Hub searcher with mirror management
|   ├── ScienceDirectSearcher.ts # ScienceDirect (Elsevier) searcher
│   ├── SpringerSearcher.ts   # Springer Nature searcher (Meta v2 & OpenAccess APIs)
|   ├── WileySearcher.ts      # Wiley TDM API searcher
|   └── ScopusSearcher.ts     # Scopus citation database searcher
├── utils/
│   └── RateLimiter.ts        # Token bucket rate limiter
└── server.ts                 # MCP server main file

Adding New Platforms

1. Create new searcher class extending PaperSource
2. Implement required abstract methods
3. Register new searcher in server.ts
4. Add corresponding MCP tool

Testing

# Run tests
npm test

# Run linting
npm run lint

# Code formatting
npm run format

🌟 Platform-Specific Features

Springer Nature Dual API System

Springer Nature provides two APIs:

1. Metadata API v2 (Main API)

   • Endpoint: https://api.springernature.com/meta/v2/json
   • Searches all Springer content (subscription + open access)
   • Requires API key from https://dev.springernature.com/

2. OpenAccess API (Optional)

   • Endpoint: https://api.springernature.com/openaccess/json
   • Only searches open access content
   • May require separate API key or special permissions
   • Better for finding downloadable PDFs

// Search all Springer content
search_springer({
  query: "machine learning",
  maxResults: 10
})

// Search only open access papers
search_springer({
  query: "COVID-19",
  openAccess: true,  // Uses OpenAccess API if available
  maxResults: 5
})

Web of Science Advanced Search

// Use Web of Science query syntax
search_webofscience({
  query: 'TS="machine learning" AND PY=2023',
  maxResults: 20
})

// Author search
search_webofscience({
  query: 'AU="Smith, J*"',
  maxResults: 10
})

// Journal search
search_webofscience({
  query: 'SO="Nature" AND PY=2022-2023',
  maxResults: 15
})

Supported Fields:

• TS: Topic search
• AU: Author
• SO: Source journal
• PY: Publication year
• DO: DOI
• TI: Title

Google Scholar Features

• Academic Paper Priority: Automatically filters out books, prioritizes peer-reviewed papers
• Citation Data: Provides citation counts and academic metrics
• Anti-Detection: Smart request patterns to avoid blocking
• Comprehensive Coverage: Searches across all academic publishers

Semantic Scholar Features

• AI-Powered Search: Semantic understanding of queries
• Citation Networks: Paper relationships and influence metrics
• Open Access PDFs: Direct links to freely available papers
• Research Fields: Filter by specific academic disciplines

Sci-Hub Features

• Universal Access: Access papers using DOI or direct URLs
• Mirror Network: Automatic detection and use of fastest available mirror (11+ mirrors)
• Health Monitoring: Continuous monitoring of mirror site availability
• Automatic Failover: Seamless switching between mirrors when one fails
• Smart Retry: Automatic retry with different mirrors on failure
• Response Time Optimization: Mirrors sorted by response time for best performance

📝 License

MIT License - see LICENSE file for details.

🤝 Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.

1. Fork the project
2. Create feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open Pull Request

🐛 Issue Reporting

If you encounter issues, please report them at GitHub Issues.

🙏 Acknowledgments

• Original paper-search-mcp for the foundation
• MCP community for the protocol standards

⭐ If this project helps you, please give it a star!