pubmed-mcp-smithery

# PubMed Enhanced Search MCP Server [](https://smithery.ai/server/@leescot/pubmed-mcp-smithery) 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: ```json "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 ```python # 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 ```python # Example mesh_terms = await get_mesh_terms("diabetes") ``` 3. `get_pubmed_count` - Get the count of publications for multiple search terms ```python # Example counts = await get_pubmed_count(["diabetes", "obesity", "hypertension"]) ``` 4. `format_paper_details` - Get detailed information about specific papers by PMID ```python # Example paper_details = await format_paper_details(["12345678", "87654321"]) ``` 5. `pico_search` - Perform structured PICO (Population, Intervention, Comparison, Outcome) searches with synonyms ```python # 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 The PICO search tool helps researchers conduct evidence-based literature searches by: 1. Allowing multiple synonym terms for each PICO element 2. Combining terms within each element using OR operators 3. Performing AND combinations between elements (P AND I, P AND I AND C, etc.) 4. Returning both search queries and publication counts for each combination This approach helps refine research questions and identify the most relevant literature. ## Rate Limiting The server implements automatic retry mechanism with backoff delays to handle potential rate limiting by NCBI's E-utilities service. ## License This project is licensed under the BSD 3-Clause License - see the LICENSE file for details.