What is Pubmed Mcp

pubmed_mcp is an advanced Model Content Protocol (MCP) server designed for searching, analyzing, and retrieving academic medical papers from the PubMed database.

Use cases

Use cases include conducting literature reviews, analyzing publication trends of researchers, generating citations for academic writing, and retrieving detailed information on specific medical publications.

How to use

To use pubmed_mcp, clone the repository, install dependencies, and start the server using the command ‘mcp run pubmed_server.py’. You can also run it in development mode or add it to your MCP client configuration.

Key features

Key features include searching for medical literature by topics and researcher names, retrieving detailed publication metadata, generating formatted citations, analyzing publication statistics, advanced error handling, and providing detailed performance metrics.

Where to use

pubmed_mcp is primarily used in the fields of medical research, healthcare, and academic institutions where access to medical literature is essential.

Medical Literature Research Tool

An advanced Model Content Protocol (MCP) server providing tools to search, analyze, and retrieve academic medical papers from the PubMed database.

Features

• Search for medical literature using topics and researcher names
• Retrieve comprehensive publication details with structured metadata
• Generate formatted citations for publications
• Analyze researcher publication statistics and patterns
• Advanced error handling with retry mechanisms
• Detailed performance metrics

Installation

1. Clone this repository:

   git clone <repository-url>
   cd medical-literature-tool
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Create a .env file in the project root if needed for configuration

Usage

1. Start the server:

   mcp run pubmed_server.py
   

   For development mode:

   mcp dev pubmed_server.py
   

2. Or add the server to your MCP client configuration.

API Tools

1. find_articles

Search for medical literature matching specified topics and researchers.

Parameters:

• topics (List[str]): Medical topics or keywords to search in titles and abstracts
• researchers (List[str]): Researcher/author names to search for
• result_limit (int): Maximum number of results to return (default: 15)

Returns:

• Dictionary with search results, metadata, and performance metrics

2. get_publication_details

Retrieve comprehensive details for a specific publication, including a formatted citation.

Parameters:

• article_id (str): PubMed ID of the article to retrieve

Returns:

• Dictionary containing detailed article metadata and citation

3. get_article_statistics

Analyze publication patterns for a specific researcher.

Parameters:

• researcher (str): Name of the researcher/author to analyze

Returns:

• Dictionary with publication statistics, including total count, top journals, and publication years

Technical Implementation

The server is built with a robust architecture:

• Object-Oriented Design: Using classes for better code organization
• Advanced Error Handling: Request retry mechanism for API reliability
• Performance Monitoring: Timing and metrics for search operations
• Enhanced Data Structures: Nested JSON responses with rich metadata
• Logging System: Rotating logs with detailed error tracking
• Modular Components: Separation of concerns between query building, API requests, and data parsing