PubMed Enhanced Search MCP Server

A Model Content Protocol server that provides enhanced tools to search and retrieve academic papers from PubMed database, with additional features such as MeSH term lookup, publication count statistics, and PICO-based evidence search.

Features

• Search PubMed by keywords with optional journal filter
• Support for sorting results by relevance or date (newest/oldest first)
• Get MeSH (Medical Subject Headings) terms related to a search word
• Get publication counts for multiple search terms (useful for comparing prevalence)
• Retrieve detailed paper information including abstract, DOI, authors, and keywords
• Perform structured PICO-based searches with support for synonyms and combination queries

Installing

Prerequisites

• Python 3.6+
• pip

Installation

1. Clone this repository:

   git clone https://github.com/leescot/pubmed-mcp-smithery
   cd pubmed-mcp-smithery
   

2. Install dependencies:

   pip install fastmcp requests
   

Usage

Running locally

Start the server:

python pubmed_enhanced_mcp_server.py

For development mode with auto-reloading:

mcp dev pubmed_enhanced_mcp_server.py

Adding to Claude Desktop

Edit your Claude Desktop configuration file (CLAUDE_DIRECTORY/claude_desktop_config.json) to add the server:

"pubmed-enhanced": {
    "command": "python",
    "args": [
        "/path/pubmed-mcp-smithery/pubmed_enhanced_mcp_server.py"
    ]
}

MCP Functions

The server provides these main functions:

1. search_pubmed - Search PubMed for articles matching keywords with optional journal filtering

   # Example
   results = await search_pubmed(
       keywords=["diabetes", "insulin resistance"],
       journal="Nature Medicine",
       num_results=5,
       sort_by="date_desc"
   )
   

2. get_mesh_terms - Look up MeSH terms related to a medical concept

   # Example
   mesh_terms = await get_mesh_terms("diabetes")
   

3. get_pubmed_count - Get the count of publications for multiple search terms

   # Example
   counts = await get_pubmed_count(["diabetes", "obesity", "hypertension"])
   

4. format_paper_details - Get detailed information about specific papers by PMID

   # Example
   paper_details = await format_paper_details(["12345678", "87654321"])
   

5. pico_search - Perform structured PICO (Population, Intervention, Comparison, Outcome) searches with synonyms

   # Example
   pico_results = await pico_search(
       p_terms=["diabetes", "type 2 diabetes", "T2DM"],
       i_terms=["metformin", "glucophage"],
       c_terms=["sulfonylurea", "glipizide"],
       o_terms=["HbA1c reduction", "glycemic control"]
   )
   

PICO Search Functionality