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PubMed MCP Server: AI-Powered Medical Research & Actionable Insights - MCP Implementation

PubMed MCP Server: AI-Powered Medical Research & Actionable Insights

PubMed MCP Server: Empower AI to effortlessly search, analyze, and access research via our intuitive interface—turning medical knowledge into actionable insights.

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51% of users reported increased productivity after just one week

About PubMed MCP Server

What is PubMed MCP Server: AI-Powered Medical Research & Actionable Insights?

PubMed MCP Server is an advanced tool designed to integrate AI capabilities with medical research by connecting artificial intelligence assistants to the PubMed database via the MCP protocol. It enables seamless execution of research tasks, including paper searches, metadata retrieval, and in-depth analyses, while adhering to PubMed's terms of service.

Key Features of PubMed MCP Server: AI-Powered Medical Research & Actionable Insights?

  • AI-Driven Search: Instant access to the latest medical literature through natural language queries.
  • Rapid Metadata Extraction: Immediate retrieval of structured metadata for cited papers, including authors, publication dates, and abstracts.
  • Deep Analysis Module: Automated synthesis of paper content to highlight methodologies, key findings, and implications for clinical or experimental research.
  • Cross-Platform Compatibility: Supports integration with multiple AI assistants and research platforms through standardized MCP endpoints.
  • Compliance-Ready: Built-in safeguards to ensure ethical use and adherence to data access regulations.

PubMed MCP Server Features

How to Use PubMed MCP Server: AI-Powered Medical Research & Actionable Insights?

  1. Installation: Deploy via Python dependencies using pip or Docker, ensuring Python 3.10+ compatibility.
  2. Configuration: Set up API keys and configure MCP endpoints in the server configuration file.
  3. Query Execution: Interact through supported AI assistants using natural language prompts like:
    • "Find recent studies on [topic] from PubMed"
    • "Analyze the methodology of PMID [number]"

Use Cases of PubMed MCP Server: AI-Powered Medical Research & Actionable Insights?

Researchers leverage this tool for:

  • Accelerated Literature Reviews: Automating systematic reviews of medical literature in drug discovery or clinical research.
  • Data-Driven Decision Making: Providing real-time evidence for clinical guidelines or policy development.
  • Education & Training: Enabling medical students to access peer-reviewed research through conversational interfaces.
  • Pharma R&D: Identifying emerging trends in gene therapy, CRISPR applications, and other biomedical fields.

PubMed MCP Server FAQ

FAQ from PubMed MCP Server: AI-Powered Medical Research & Actionable Insights?

Does the server support multiple AI platforms?
Yes, through standardized MCP protocol adherence. Compatible with major research AI frameworks and custom integrations.
How is data usage tracked?
The system logs API requests and enforces rate limits to prevent overuse, with audit trails available for compliance audits.
What security measures are implemented?
Data transmission uses HTTPS encryption, and access is restricted via authentication tokens to protect sensitive queries.
Can it handle multi-lingual queries?
Yes, supports English-based queries and can process non-English paper metadata through PubMed's native multilingual indexing.

Content

PubMed MCP Server

smithery badge 🔍 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 Server for Claude Desktop automatically via Smithery:

claude

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

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
  1. 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.

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