📚 Academic MCP

English | 中文

🔬 academic-mcp is a Python-based MCP server that enables users to search, download, and read academic papers from various platforms. It provides three main tools:

• 🔎 paper_search: Search papers across multiple academic databases
• 📥 paper_download: Download paper PDFs, return paths of downloaded files
• 📖 paper_read: Extract and read text content from papers

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📑 Table of Contents

• 🎬 Screenshot
• 📝 TODO
• ✨ Features
• 📦 Installation
  • ⚡ Quick Start
  • 🛠️ For Development
• 🚀 Usage
  • 🔎 Search Papers
  • 📥 Download Papers
  • 📖 Read Papers
  • ⚙️ Environment Variables
• 🤝 Contributing
• 📄 License

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✨ Features

• 🌐 Multi-Source Support: Search and download papers from arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, IACR ePrint Archive, Semantic Scholar, and CrossRef.
• 🎯 Unified Interface: All platforms accessible through consistent paper_search, paper_download, and paper_read tools.
• 📊 Standardized Output: Papers are returned in a consistent dictionary format via the Paper class.
• ⚡ Asynchronous Operations: Efficiently handles concurrent searches and downloads using httpx and async/await.
• 🔌 MCP Integration: Compatible with MCP clients for LLM context enhancement.
• 🧩 Extensible Design: Easily add new academic platforms by extending the sources module.

🎬 Screenshot

<img src="assets/screenshot.png" alt="Screenshot" width="800">

📝 TODO

Planned Academic Platforms

• [x] arXiv
• [x] PubMed
• [x] bioRxiv
• [x] medRxiv
• [x] Google Scholar
• [x] IACR ePrint Archive
• [x] Semantic Scholar
• [x] CrossRef
• [ ] PubMed Central (PMC)
• [ ] Science Direct
• [ ] Springer Link
• [ ] IEEE Xplore
• [ ] ACM Digital Library
• [ ] Web of Science
• [ ] Scopus
• [ ] JSTOR
• [ ] ResearchGate
• [ ] CORE
• [ ] Microsoft Academic

📦 Installation

academic-mcp can be installed using uv or pip. Below are two approaches: a quick start for immediate use and a detailed setup for development.

⚡ Quick Start

For users who want to quickly run the server:

1. Install Package:

   pip install academic-mcp
   

2. Configure Claude Desktop: Add this configuration to ~/Library/Application Support/Claude/claude_desktop_config.json (Mac) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

   {
     "mcpServers": {
       "academic-mcp": {
         "command": "python",
         "args": [
           "-m",
           "academic_mcp"
         ],
         "env": {
           "SEMANTIC_SCHOLAR_API_KEY": "",
           "ACADEMIC_MCP_DOWNLOAD_PATH": "./downloads"
         }
       }
     }
   }
   

   Note: The SEMANTIC_SCHOLAR_API_KEY is optional and only required for enhanced Semantic Scholar features.

🛠️ For Development

For developers who want to modify the code or contribute:

1. Setup Environment:

   # Install uv if not installed
   curl -LsSf https://astral.sh/uv/install.sh | sh
   
   # Clone repository
   git clone https://github.com/LinXueyuanStdio/academic-mcp.git
   cd academic-mcp
   
   # Create and activate virtual environment
   uv venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   

2. Install Dependencies:

   # Install dependencies (recommended)
   uv pip install -e .
   
   # Add development dependencies (optional)
   uv pip install pytest flake8
   

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🚀 Usage

Once configured, academic-mcp provides three main tools accessible through Claude Desktop or any MCP-compatible client:

1. Search Papers (paper_search)

Search for academic papers across multiple sources:

# Search arXiv for machine learning papers
paper_search([
    {"searcher": "arxiv", "query": "machine learning", "max_results": 5}
])

# Search multiple platforms simultaneously
paper_search([
    {"searcher": "arxiv", "query": "deep learning", "max_results": 5},
    {"searcher": "pubmed", "query": "cancer immunotherapy", "max_results": 3},
    {"searcher": "semantic", "query": "climate change", "max_results": 4, "year": "2020-2023"}
])

# Search all platforms (omit "searcher" parameter)
paper_search([
    {"query": "quantum computing", "max_results": 10}
])

2. Download Papers (paper_download)

Download paper PDFs using their identifiers:

paper_download([
    {"searcher": "arxiv", "paper_id": "2106.12345"},
    {"searcher": "pubmed", "paper_id": "32790614"},
    {"searcher": "biorxiv", "paper_id": "10.1101/2020.01.01.123456"},
    {"searcher": "semantic", "paper_id": "DOI:10.18653/v1/N18-3011"}
])

3. Read Papers (paper_read)

Extract and read text content from papers:

# Read an arXiv paper
paper_read(searcher="arxiv", paper_id="2106.12345")

# Read a PubMed paper
paper_read(searcher="pubmed", paper_id="32790614")

# Read a Semantic Scholar paper
paper_read(searcher="semantic", paper_id="DOI:10.18653/v1/N18-3011")

Environment Variables

• SEMANTIC_SCHOLAR_API_KEY: Optional API key for enhanced Semantic Scholar features
• ACADEMIC_MCP_DOWNLOAD_PATH: Directory for downloaded PDFs (default: ./downloads)

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🤝 Contributing

We welcome contributions! Here's how to get started:

1. Fork the Repository: Click "Fork" on GitHub.

2. Clone and Set Up:

   git clone https://github.com/yourusername/academic-mcp.git
   cd academic-mcp
   uv pip install -e .  # Install in development mode
   

3. Make Changes:

   • Add new platforms in academic_mcp/sources/.
   • Update tests in tests/.

4. Submit a Pull Request: Push changes and create a PR on GitHub.

📄 License

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

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Happy researching with academic-mcp! If you encounter issues, open a GitHub issue.