What is Pubmed Mcp Server

The PubMed MCP Server is a bridge that connects AI assistants to PubMed’s extensive biomedical literature using the Model Context Protocol (MCP). It enables AI models to search for, access, and analyze scientific articles programmatically.

Use cases

It is useful for researchers and AI assistants needing to access scientific literature. Users can perform paper searches, retrieve metadata, access and download full-text articles, and conduct deep analyses of research papers.

How to use

Users can install the PubMed MCP Server via the Smithery platform or clone the GitHub repository. After installing dependencies, they can start the server and configure it for use with their AI assistant. Specific commands and JSON configurations are provided for different systems.

Key features

Core features include paper search with keywords, efficient retrieval of metadata, access to full-text PDFs, and tools for deep analysis. Specialized prompts are provided to assist in the analysis of papers.

Where to use

The PubMed MCP Server can be integrated with AI assistants like Claude Desktop, Cursor, Windsurf, and CLine, allowing users to enhance their research and analysis capabilities directly within these environments.

PubMed MCP Server

🔍 Enable AI assistants to search, access, and analyze PubMed articles through a simple MCP interface.

The PubMed MCP Server provides a bridge between AI assistants and PubMed’s vast repository of biomedical literature through the Model Context Protocol (MCP). It allows AI models to search for scientific articles, access their metadata, and perform deep analysis in a programmatic way.

🤝 Contribute • 📝 Report Bug

✨ Core Features

• 🔎 Paper Search: Query PubMed articles with keywords or advanced search ✅
• 🚀 Efficient Retrieval: Fast access to paper metadata ✅
• 📊 Metadata Access: Retrieve detailed metadata for specific papers ✅
• 📊 Research Support: Facilitate biomedical sciences research and analysis ✅
• 📄 Paper Access: Attempt to download full-text PDF content ✅
• 🧠 Deep Analysis: Perform comprehensive analysis of papers ✅
• 📝 Research Prompts: A set of specialized prompts for paper analysis ✅

🚀 Quick Start

Prerequisites

• Python 3.10+
• FastMCP library

Installation

Installing via Smithery

To install pubmed-mcp-server for Claude Desktop automatically via Smithery:

claude

npx -y @smithery/cli install @JackKuo666/pubmed-mcp-server --client claude

Cursor

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

• Mac/Linux

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

Windsurf

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

CLine

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

1. Clone the repository:

   git clone https://github.com/JackKuo666/PubMed-MCP-Server.git
   cd PubMed-MCP-Server
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

📊 Usage

Start the MCP server:

python pubmed_server.py

Usage with Claude Desktop

Add this configuration to your claude_desktop_config.json:

(Mac OS)

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

(Windows version):

{
  "mcpServers": {
    "pubmed": {
      "command": "C:\\Users\\YOUR\\PATH\\miniconda3\\envs\\mcp_server\\python.exe",
      "args": [
        "D:\\code\\YOUR\\PATH\\PubMed-MCP-Server\\pubmed_server.py"
      ],
      "env": {},
      "disabled": false,
      "autoApprove": []
    }
  }
}

Using with Cline

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

🛠 MCP Tools

The PubMed MCP Server provides the following tools:

1. search_pubmed_key_words: Search for articles on PubMed using keywords.
2. search_pubmed_advanced: Perform an advanced search for articles on PubMed with multiple parameters.
3. get_pubmed_article_metadata: Fetch metadata for a PubMed article using its PMID.
4. download_pubmed_pdf: Attempt to download the full-text PDF for a PubMed article.
5. deep_paper_analysis: Perform a comprehensive analysis of a PubMed article.

Searching Papers

You can ask the AI assistant to search for papers using queries like:

Can you search PubMed for recent papers about CRISPR?

Getting Paper Details

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

Can you show me the metadata for the paper with PMID 12345678?

Analyzing Papers

You can request a deep analysis of a paper:

Can you perform a deep analysis of the paper with PMID 12345678?

📁 Project Structure

• pubmed_server.py: The main MCP server implementation using FastMCP
• pubmed_web_search.py: Contains the logic for searching PubMed and retrieving article information

🔧 Dependencies

• Python 3.10+
• FastMCP
• asyncio
• logging
• requests
• beautifulsoup4

🤝 Contributing

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

📄 License

This project is licensed under the MIT License.

⚠️ Disclaimer

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