Content
# medRxiv MCP Server
[](https://smithery.ai/server/@JackKuo666/medrxiv-mcp-server)
🔍 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
## ✨ Core Features
- 🔎 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 📝
## 🚀 Quick Start
### Installing via Smithery
To install medRxiv Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@JackKuo666/medrxiv-mcp-server):
#### claude
```bash
npx -y @smithery/cli@latest install @JackKuo666/medrxiv-mcp-server --client claude --config "{}"
```
#### Cursor
Paste the following into Settings → Cursor Settings → MCP → Add new server:
- Mac/Linux
```s
npx -y @smithery/cli@latest run @JackKuo666/medrxiv-mcp-server --client cursor --config "{}"
```
#### Windsurf
```sh
npx -y @smithery/cli@latest install @JackKuo666/medrxiv-mcp-server --client windsurf --config "{}"
```
### CLine
```sh
npx -y @smithery/cli@latest install @JackKuo666/medrxiv-mcp-server --client cline --config "{}"
```
### Installing Manually
Install using uv:
```bash
uv tool install medRxiv-mcp-server
```
For development:
```bash
# 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
```
## 📊 Usage
Start the MCP server:
```bash
python medrxiv_server.py
```
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
```python
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
```python
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
```python
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.
## 🛠 MCP Tools
The medRxiv MCP Server provides the following tools:
### search_medrxiv_key_words
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
### search_medrxiv_advanced
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
### get_medrxiv_metadata
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)
```json
{
"mcpServers": {
"medrxiv": {
"command": "python",
"args": ["-m", "medrxiv-mcp-server"]
}
}
}
```
(Windows version):
```json
{
"mcpServers": {
"medrxiv": {
"command": "C:\\Users\\YOUR_USERNAME\\AppData\\Local\\Programs\\Python\\Python311\\python.exe",
"args": [
"-m",
"medrxiv-mcp-server"
]
}
}
}
```
Using with Cline
```json
{
"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:
### Searching Papers
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
### Getting Paper Details
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)
## 📝 TODO
### download_paper
Download a paper and save it locally.
### read_paper
Read the content of a downloaded paper.
### list_papers
List all downloaded papers.
### 📝 Research Prompts
The server offers specialized prompts to help analyze academic papers:
#### Paper Analysis Prompt
A comprehensive workflow for analyzing academic papers that only requires a paper ID:
```python
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
## 📁 Project Structure
- `medrxiv_server.py`: The main MCP server implementation using FastMCP
- `medrxiv_web_search.py`: Contains the web scraping logic for searching medRxiv
## 🔧 Dependencies
- 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:
```bash
pip install FastMCP requests beautifulsoup4
```
## 🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## 📄 License
This project is licensed under the MIT License.
## 🙏 Acknowledgements
This project is inspired by and built upon the work done in the [arxiv-mcp-server](https://github.com/blazickjp/arxiv-mcp-server) project.
## ⚠️ Disclaimer
This tool is for research purposes only. Please respect medRxiv's terms of service and use this tool responsibly.
