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PubMed: Fast & Trusted Biomedical Research Access - MCP Implementation

PubMed: Fast & Trusted Biomedical Research Access

Find trusted biomedical research fast: PubMed connects you to millions of studies, journals, and data – your breakthrough starts here. Simple, powerful, and free." )

Research And Data
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About PubMed

What is PubMed: Fast & Trusted Biomedical Research Access?

PubMed MCP Server acts as a bridge between AI assistants and the PubMed database, enabling seamless access to biomedical research. It leverages the Model Context Protocol (MCP) to streamline tasks like paper searches, metadata extraction, and deep analysis. Designed for researchers, it ensures quick, reliable access to peer-reviewed studies while adhering to PubMed’s terms of service.

How to use PubMed: Fast & Trusted Biomedical Research Access?

1. Install Dependencies: Ensure Python 3.10+ and required libraries like FastMCP and requests are installed.
2. Configure Clients: Set up your AI assistant or script to interact with the server via MCP endpoints.
3. Launch the Server: Run pubmed_server.py to activate the service.
4. Query Examples: Use natural language prompts like “Search for CRISPR studies from 2023” or request metadata via PMID.

PubMed Features

Key Features of PubMed: Fast & Trusted Biomedical Research Access?

  • Real-Time Search: Instant access to over 33 million citations and abstracts.
  • Metadata Extraction: Retrieve structured data including authors, journals, and publication dates.
  • Deep Analysis: AI-driven summaries and critical evaluations of paper methodologies.
  • Cross-Platform Support: Works with popular AI tools like ChatGPT and custom scripts.
  • Compliance-First Design: Built-in rate-limiting to prevent API misuse.

Use cases of PubMed: Fast & Trusted Biomedical Research Access?

Researchers use this tool to:
• Rapidly compile literature reviews for grants or publications.
• Validate experimental approaches by cross-referencing peer-reviewed methods.
• Monitor emerging trends in fields like gene editing or drug discovery.

PubMed FAQ

FAQ from PubMed: Fast & Trusted Biomedical Research Access?

Q: Is this compatible with Python 3.9?
A: No. The server requires Python 3.10+ due to asyncio optimizations.

Q: Can I use this for clinical trials data?
A: Yes, but ensure queries comply with PubMed’s clinical studies access policies.

Q: What if the API returns an error?
A: Check your PMID validity or contact [email protected] for server-side issues.

Q: How do I contribute code?
A: Fork the repository on GitHub and submit pull requests for review.

Q: Are user queries logged?
A: No. The server follows strict privacy guidelines—only aggregated stats are tracked.

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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