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JackKuo666/medRxiv-MCP-Server: 🔍 Enable AI assistants to search and access medRxiv papers through a simple MCP interface.

🔍 Enable AI assistants to search and access medRxiv papers through a simple MCP interface.

The medRxiv MCP Server provides a bridge between AI assistants and medRxiv's preprint repository through the Model Context Protocol (MCP). It allows AI models to search for health sciences preprints and access their content in a programmatic way.

🤝 Contribute • 📝 Report Bug

🔎 Paper Search: Query medRxiv papers with custom search strings or advanced search parameters ✅
🚀 Efficient Retrieval: Fast access to paper metadata ✅
📊 Metadata Access: Retrieve detailed metadata for specific papers using DOI ✅
📊 Research Support: Facilitate health sciences research and analysis ✅
📄 Paper Access: Download and read paper content 📝
📋 Paper Listing: View all downloaded papers 📝
🗃️ Local Storage: Papers are saved locally for faster access 📝
📝 Research Prompts: A set of specialized prompts for paper analysis 📝

To install medRxiv Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli@latest install @JackKuo666/medrxiv-mcp-server --client claude --config "{}"

Paste the following into Settings → Cursor Settings → MCP → Add new server:

Mac/Linux

npx -y @smithery/cli@latest run @JackKuo666/medrxiv-mcp-server --client cursor --config "{}"

npx -y @smithery/cli@latest install @JackKuo666/medrxiv-mcp-server --client windsurf --config "{}"

npx -y @smithery/cli@latest install @JackKuo666/medrxiv-mcp-server --client cline --config "{}"

Install using uv:

uv tool install medRxiv-mcp-server

For development:

# Clone and set up development environment
git clone https://github.com/JackKuo666/medRxiv-MCP-Server.git
cd medRxiv-MCP-Server

# Create and activate virtual environment
uv venv
source .venv/bin/activate
uv pip install -r requirements.txt

Start the MCP server:

Once the server is running, you can use the provided MCP tools in your AI assistant or application. Here are some examples of how to use the tools:

Example 1: Search for papers using keywords

result = await mcp.use_tool("search_medrxiv_key_words", {
    "key_words": "COVID-19 vaccine efficacy",
    "num_results": 5
})
print(result)

Example 2: Perform an advanced search

result = await mcp.use_tool("search_medrxiv_advanced", {
    "term": "COVID-19",
    "author1": "MacLachlan",
    "start_date": "2020-01-01",
    "end_date": "2023-12-31",
    "num_results": 3
})
print(result)

Example 3: Get metadata for a specific paper

result = await mcp.use_tool("get_medrxiv_metadata", {
    "doi": "10.1101/2025.03.09.25323517"
})
print(result)

These examples demonstrate how to use the three main tools provided by the medRxiv MCP Server. Adjust the parameters as needed for your specific use case.

The medRxiv MCP Server provides the following tools:

Search for articles on medRxiv using key words.

Parameters:

key_words (str): Search query string
num_results (int, optional): Number of results to return (default: 10)

Returns: List of dictionaries containing article information

Perform an advanced search for articles on medRxiv.

Parameters:

term (str, optional): General search term
title (str, optional): Search in title
author1 (str, optional): First author
author2 (str, optional): Second author
abstract_title (str, optional): Search in abstract and title
text_abstract_title (str, optional): Search in full text, abstract, and title
section (str, optional): Section of medRxiv
start_date (str, optional): Start date for search range (format: YYYY-MM-DD)
end_date (str, optional): End date for search range (format: YYYY-MM-DD)
num_results (int, optional): Number of results to return (default: 10)

Returns: List of dictionaries containing article information

Fetch metadata for a medRxiv article using its DOI.

Parameters:

doi (str): DOI of the article

Returns: Dictionary containing article metadata

Usage with Claude Desktop

Add this configuration to your claude_desktop_config.json:

(Mac OS)

{
  "mcpServers": {
    "medrxiv": {
      "command": "python",
      "args": ["-m", "medrxiv-mcp-server"]
      }
  }
}

(Windows version):

{
  "mcpServers": {
    "medrxiv": {
      "command": "C:\\Users\\YOUR_USERNAME\\AppData\\Local\\Programs\\Python\\Python311\\python.exe",
      "args": [
        "-m",
        "medrxiv-mcp-server"
      ]
    }
  }
}

Using with Cline

{
  "mcpServers": {
    "medrxiv": {
      "command": "bash",
      "args": [
        "-c",
        "source /home/YOUR/PATH/mcp-server-medRxiv/.venv/bin/activate && python /home/YOUR/PATH/mcp-server-medRxiv/medrxiv_server.py"
      ],
      "env": {},
      "disabled": false,
      "autoApprove": []
    }
  }
}

After restarting Claude Desktop, the following capabilities will be available:

You can ask Claude to search for papers using queries like:

Can you search medRxiv for recent papers about genomics?

The search will return basic information about matching papers including:

• Paper title

• Authors

• DOI

Once you have a DOI, you can ask for more details:

Can you show me the details for paper 10.1101/003541?

This will return:

• Full paper title

• Authors

• Publication date

• Paper abstract

• Links to available formats (PDF/HTML)

Download a paper and save it locally.

Read the content of a downloaded paper.

List all downloaded papers.

The server offers specialized prompts to help analyze academic papers:

A comprehensive workflow for analyzing academic papers that only requires a paper ID:

result = await call_prompt("deep-paper-analysis", {
    "paper_id": "2401.12345"
})

This prompt includes:

Detailed instructions for using available tools (list_papers, download_paper, read_paper, search_papers)
A systematic workflow for paper analysis
Comprehensive analysis structure covering:
- Executive summary
- Research context
- Methodology analysis
- Results evaluation
- Practical and theoretical implications
- Future research directions
- Broader impacts

medrxiv_server.py: The main MCP server implementation using FastMCP
medrxiv_web_search.py: Contains the web scraping logic for searching medRxiv

Python 3.10+
FastMCP
asyncio
logging
requests (for web scraping, used in medrxiv_web_search.py)
beautifulsoup4 (for web scraping, used in medrxiv_web_search.py)

You can install the required dependencies using:

pip install FastMCP requests beautifulsoup4

Contributions are welcome! Please feel free to submit a Pull Request.

This project is licensed under the MIT License.

This project is inspired by and built upon the work done in the arxiv-mcp-server project.

This tool is for research purposes only. Please respect medRxiv's terms of service and use this tool responsibly.

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