PubMed MCP Server

A comprehensive Model Context Protocol (MCP) server for PubMed literature search and management. This server provides advanced search capabilities, citation formatting, and research analysis tools through the MCP protocol.

Features

Advanced PubMed Search: Search with complex filters including date ranges, article types, authors, journals, and MeSH terms
Article Details: Retrieve detailed information for specific PMIDs including abstracts, authors, and metadata
Citation Export: Export citations in multiple formats (BibTeX, APA, MLA, Chicago, Vancouver, EndNote, RIS)
Author Search: Find articles by specific authors with co-author information
Related Articles: Discover articles related to a specific PMID
MeSH Term Search: Search and explore Medical Subject Headings
Journal Analysis: Get metrics and recent articles from specific journals
Research Trends: Analyze publication trends over time
Article Comparison: Compare multiple articles side by side
Caching: Built-in caching for improved performance
Rate Limiting: Respectful API usage with configurable rate limits

Installation

Prerequisites

Python 3.8 or higher
NCBI API key (free registration required)
Valid email address for NCBI API identification

Quick Start

Clone the repository:
git clone https://github.com/your-org/pubmed-mcp.git cd pubmed-mcp
Install dependencies:
pip install -r requirements.txt
Set up environment variables:
cp env.example .env # Edit .env with your NCBI API key and email
Run the server:
python -m src.main

Development Installation

For development with additional tools:

make install-dev

Or manually:

pip install -r requirements.txt
pip install -e .
pip install black isort mypy flake8

Configuration

Create a .env file in the project root with the following variables:

# Required
PUBMED_API_KEY=your_ncbi_api_key_here
PUBMED_EMAIL=your.email@example.com

# Optional
CACHE_TTL=300
CACHE_MAX_SIZE=1000
RATE_LIMIT=3.0
LOG_LEVEL=info

Getting an NCBI API Key

Visit NCBI Account Settings
Sign in or create an account
Navigate to "API Key Management"
Create a new API key
Copy the key to your .env file

Usage

Available Tools

The server provides the following MCP tools:

1. `search_pubmed`

Search PubMed with advanced filtering options.

{
  "query": "machine learning healthcare",
  "max_results": 20,
  "date_range": "5y",
  "article_types": ["Journal Article", "Review"],
  "has_abstract": true
}

2. `get_article_details`

Get detailed information for specific PMIDs.

{
  "pmids": ["12345678", "87654321"],
  "include_abstracts": true,
  "include_citations": false
}

3. `search_by_author`

Search for articles by a specific author.

{
  "author_name": "Smith J",
  "max_results": 10,
  "include_coauthors": true
}

4. `export_citations`

Export citations in various formats.

{
  "pmids": ["12345678"],
  "format": "bibtex",
  "include_abstracts": false
}

5. `find_related_articles`

Find articles related to a specific PMID.

{
  "pmid": "12345678",
  "max_results": 10
}

6. `search_mesh_terms`

Search using MeSH terms.

{
  "term": "Machine Learning",
  "max_results": 20
}

7. `analyze_research_trends`

Analyze publication trends over time.

{
  "topic": "artificial intelligence",
  "years_back": 5,
  "include_subtopics": false
}

Example Usage with MCP Client

import asyncio
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

async def main():
    server_params = StdioServerParameters(
        command="python",
        args=["-m", "src.main"]
    )

    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            # Initialize the session
            await session.initialize()

            # Search PubMed
            result = await session.call_tool(
                "search_pubmed",
                {
                    "query": "COVID-19 vaccines",
                    "max_results": 5,
                    "date_range": "1y"
                }
            )

            print(result.content[0].text)

if __name__ == "__main__":
    asyncio.run(main())

Development

Running Tests

# Run all tests
make test

# Run with coverage
make test-coverage

# Run specific test types
python run_tests.py unit
python run_tests.py integration
python run_tests.py coverage

Code Quality

# Format code
make format

# Run linting
make lint

# Type checking
mypy src/

Project Structure

pubmed-mcp/
├── src/
│   ├── __init__.py
│   ├── main.py              # Entry point
│   ├── server.py            # MCP server implementation
│   ├── models.py            # Pydantic models
│   ├── pubmed_client.py     # PubMed API client
│   ├── tool_handler.py      # Tool request handlers
│   ├── citation_formatter.py # Citation formatting
│   ├── tools.py             # Tool definitions
│   └── utils.py             # Utility functions
├── tests/                   # Test suite
├── requirements.txt         # Dependencies
├── setup.py                 # Package setup
├── pyproject.toml          # Modern Python config
├── Makefile                # Development commands
├── Dockerfile              # Container setup
└── README.md               # This file

Docker

Build and Run

# Build Docker image
make docker-build

# Run with environment variables
make docker-run PUBMED_API_KEY=your_key PUBMED_EMAIL=your_email

Docker Compose

version: '3.8'
services:
  pubmed-mcp:
    build: .
    environment:
      - PUBMED_API_KEY=your_key
      - PUBMED_EMAIL=your_email
      - LOG_LEVEL=info
    volumes:
      - ./data:/app/data

API Reference

Search Parameters

query: Search query using PubMed syntax
max_results: Maximum number of results (1-200)
sort_order: Sort order (relevance, pub_date, author, journal, title)
date_from/date_to: Date range filters
date_range: Predefined ranges (1y, 5y, 10y, all)
article_types: Filter by publication types
authors: Filter by author names
journals: Filter by journal names
mesh_terms: Filter by MeSH terms
language: Language filter (e.g., 'eng', 'fre')
has_abstract: Only articles with abstracts
has_full_text: Only articles with full text
humans_only: Only human studies

Citation Formats

bibtex: BibTeX format
apa: APA style
mla: MLA style
chicago: Chicago style
vancouver: Vancouver style
endnote: EndNote format
ris: RIS format

Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests for new functionality
Run the test suite
Submit a pull request

Development Guidelines

Follow PEP 8 style guidelines
Add type hints to all functions
Write comprehensive tests
Update documentation for new features
Use conventional commit messages

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

Issues: GitHub Issues
Documentation: Project Wiki
Discussions: GitHub Discussions

Acknowledgments

NCBI E-utilities for PubMed API access
Model Context Protocol for the MCP specification
Anthropic for MCP development and support

Changelog

See CHANGELOG.md for a detailed history of changes.

Note: This server requires a valid NCBI API key and follows NCBI's usage guidelines. Please be respectful of API rate limits and terms of service.

Install Server

HTTP connection URL

security – no known vulnerabilities

license - permissive license

quality - confirmed to work

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

A comprehensive Model Context Protocol server that enables advanced PubMed literature search, citation formatting, and research analysis through natural language interactions.

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