PubMed Medical Literature Research

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