
A Model Context Protocol (MCP) server that enables AI assistants to search and analyze PubMed medical literature with advanced filtering, citations, and research tools.
概览
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. - : Search with complex filters including date ranges, article types, authors, journals, and MeSH terms - : Retrieve detailed information for specific PMIDs including abstracts, authors, and metadata - : Export citations in multiple formats (BibTeX, APA, MLA, Chicago, Vancouver, EndNote, RIS) - : Find articles by specific authors with co-author information - : Discover articles related to a specific PMID - : Search and explore Medical Subject Headings - : Get metrics and recent articles from specific journals - : Analyze publication trends over time - : Compare multiple articles side by side - : Built-in caching for improved performance - : Respectful API usage with configurable rate limits - Python 3.
README
以下为原文 README 节选,完整内容请查看原文。
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
[email protected]
# 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
.envfile
Usage
Available Tools
The server provides the following MCP tools:
1. search_pubmed
Search PubMed with advanced filtering options.
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安装
This server does not publish a one-line install command.
Open the repository installation guide 新手?先看 mcpskill.org 教程 →