Paper Search MCP Openai

🚀 Paper Search MCP

A Model Context Protocol (MCP) server for searching and downloading academic papers from multiple sources, seamlessly integrated with large language models like Claude Desktop.

🚀 Quick Start

paper-search-mcp is a Python-based MCP server that allows users to search and download academic papers from various platforms. It offers tools for paper searching and PDF downloading, which is perfect for researchers and AI-driven workflows. Built with the MCP Python SDK, it can be easily integrated with LLM clients such as Claude Desktop.

✨ Features

• Multi-Source Support: Search and download papers from arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, IACR ePrint Archive, Semantic Scholar.
• Deep Research Ready: Provides the standardized search and fetch tools required by OpenAI Deep Research and ChatGPT connectors.
• Standardized Output: Papers are returned in a consistent dictionary format via the Paper class.
• Asynchronous Tools: Efficiently handles network requests using httpx.
• MCP Integration: Compatible with MCP clients for LLM context enhancement.
• Extensible Design: Easily add new academic platforms by extending the academic_platforms module.

📦 Installation

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

Installing via Smithery

To install paper-search-mcp for Claude Desktop automatically via Smithery:

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

Quick Start

For users who want to quickly run the server:

1. Install Package:

uv add paper-search-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": {
    "paper_search_server": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/your/paper-search-mcp",
        "-m",
        "paper_search_mcp.server"
      ],
      "env": {
        "SEMANTIC_SCHOLAR_API_KEY": "" // Optional: For enhanced Semantic Scholar features
      }
    }
  }
}

⚠️ Important Note

Replace /path/to/your/paper-search-mcp with your actual installation path.

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/openags/paper-search-mcp.git
cd paper-search-mcp

# Create and activate virtual environment
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

2. Install Dependencies:

# Install project in editable mode
uv add -e .

# Add development dependencies (optional)
uv add pytest flake8

🤝 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/paper-search-mcp.git
cd paper-search-mcp
pip install -e ".[dev]"  # Install dev dependencies (if added to pyproject.toml)

3. Make Changes:
   • Add new platforms in academic_platforms/.
   • Update tests in tests/.
4. Submit a Pull Request: Push changes and create a PR on GitHub.

📋 TODO

Planned Academic Platforms

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

📄 License

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

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