MCP Connector

Access and Analyze PubMed Biomedical Literature

PubMed MCP Server lets AI assistants search, retrieve metadata for, and deep-analyze biomedical articles from PubMed.

Works with pubmed

91
Spark score
out of 100
Updated May 2025
Source checked Sep 10, 2026
Version 1.0.0
Models
universal

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Why it matters

Programmatically search, retrieve metadata, and perform deep analysis on biomedical articles from PubMed. This asset acts as a bridge between AI assistants and vast scientific literature.

Outcomes

What it gets done

01

Search PubMed articles by keywords and advanced parameters.

02

Retrieve metadata and attempt to download full-text PDFs for articles.

03

Perform comprehensive analysis on selected PubMed articles.

04

Integrate with AI assistants for programmatic research.

Source

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Capabilities

Tools your agent gets

search_pubmed_key_words

Search PubMed articles by keywords

search_pubmed_advanced

Advanced search of PubMed articles with multiple parameters

get_pubmed_article_metadata

Get PubMed article metadata by its PMID

download_pubmed_pdf

Attempt to download full-text PDF for a PubMed article

deep_paper_analysis

Comprehensive analysis of a PubMed article

Overview

PubMed MCP Server

PubMed MCP Server exposes five tools for searching PubMed, fetching article metadata by PMID, attempting full-text PDF downloads, and running deep analysis of biomedical papers through an AI assistant. Use it for literature review or research workflows that need PubMed search results, article metadata, or a structured analysis of a specific paper.

What it does

The PubMed MCP Server bridges AI assistants and PubMed's repository of biomedical literature through the Model Context Protocol. It lets an AI model search for scientific articles, access their metadata, attempt to download full-text PDFs, and perform deep analysis of a paper programmatically, supporting biomedical research and analysis workflows without leaving the conversation.

When to use - and when NOT to

Use this when a workflow needs to search PubMed for recent literature on a topic, pull metadata for a specific article by PMID, or run a structured deep analysis of a paper's content. It is scoped to PubMed specifically - it is not a general web search or a multi-database literature tool - and full-text PDF retrieval is best-effort (an attempt, not a guarantee), since not all articles have an accessible full-text PDF. The project's own disclaimer notes it is for research purposes only, and users are expected to respect PubMed's terms of service.

Inputs and outputs

Five tools are exposed: search_pubmed_key_words for keyword search, search_pubmed_advanced for advanced multi-parameter search, get_pubmed_article_metadata to fetch metadata by PMID, download_pubmed_pdf to attempt a full-text PDF download for a given article, and deep_paper_analysis to run a comprehensive analysis of a specific paper. The server also ships a set of specialized prompts for paper analysis alongside these tools.

Integrations

Requires Python 3.10+ and the FastMCP library. Installable via Smithery for Claude Desktop, Cursor, Windsurf, or Cline, or manually by cloning the repository and running pip install -r requirements.txt, then starting the server with python pubmed_server.py. Claude Desktop configuration points at the server module directly (python -m pubmed-mcp-server on macOS, or a full interpreter and script path on Windows), and Cline configuration can source a Python virtual environment before launching the script. The implementation itself lives in pubmed_server.py (the FastMCP server) and pubmed_web_search.py (PubMed search and retrieval logic).

Typical use looks like asking the assistant to search PubMed for recent papers on a topic (for example CRISPR), then, once a PMID is in hand, asking for that article's metadata, and finally requesting a deep analysis of the same paper - a search-then-drill-down pattern the five tools are built to support end to end. The project is licensed under the MIT License.

Who it's for

Researchers and anyone building an AI-assisted literature review or biomedical research workflow who wants to search PubMed, pull article metadata, and run deep paper analysis directly from an AI assistant conversation.

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

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PubMed MCP Server

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🔍 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.

FAQ

Common questions

Discussion

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