This server wraps eight academic search APIs into a unified interface for finding and downloading research papers. It exposes standardized search and fetch tools that work across arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, Semantic Scholar, CrossRef, and IACR ePrint Archive, returning consistent Paper objects regardless of source. Built for OpenAI Deep Research compatibility, so you can plug it into Claude or ChatGPT workflows that need to pull academic sources. Handles async requests with httpx and includes PDF download capabilities where available. Reach for this when you're doing literature reviews or need LLMs to cite actual papers rather than hallucinate references.
A Model Context Protocol (MCP) server for searching and downloading academic papers from multiple sources, including arXiv, PubMed, bioRxiv, and Sci-Hub (optional). Designed for seamless integration with large language models like Claude Desktop.
paper-search-mcp is a Python-based MCP server that enables users to search and download academic papers from various platforms. It provides tools for searching papers (e.g., search_arxiv) and downloading PDFs (e.g., download_arxiv), making it ideal for researchers and AI-driven workflows. Built with the MCP Python SDK, it integrates seamlessly with LLM clients like Claude Desktop.
search and fetch tools required by OpenAI Deep Research and ChatGPT connectors.Paper class.httpx.academic_platforms module.paper-search-mcp can be installed using uv or pip. Below are two approaches: a quick start for immediate use and a detailed setup for development.
To install paper-search-mcp for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @openags/paper-search-mcp --client claude
For users who want to quickly run the server:
Install Package:
uv add paper-search-mcp
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
}
}
}
}
Note: Replace
/path/to/your/paper-search-mcpwith your actual installation path.
For developers who want to modify the code or contribute:
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
Install Dependencies:
# Install project in editable mode
uv add -e .
# Add development dependencies (optional)
uv add pytest flake8
We welcome contributions! Here's how to get started:
Fork the Repository: Click "Fork" on GitHub.
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)
Make Changes:
academic_platforms/.tests/.Submit a Pull Request: Push changes and create a PR on GitHub.
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.
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