{
  "markdown": "# PubMed Enhanced Search MCP Server\n\n[![smithery badge](https://smithery.ai/badge/@leescot/pubmed-mcp-smithery)](https://smithery.ai/server/@leescot/pubmed-mcp-smithery)\n\nA 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.\n\n## Features\n\n- Search PubMed by keywords with optional journal filter\n- Support for sorting results by relevance or date (newest/oldest first)\n- Get MeSH (Medical Subject Headings) terms related to a search word\n- Get publication counts for multiple search terms (useful for comparing prevalence)\n- Retrieve detailed paper information including abstract, DOI, authors, and keywords\n- Perform structured PICO-based searches with support for synonyms and combination queries\n\n## Installing\n\n### Prerequisites\n\n- Python 3.6+\n- pip\n\n### Installation\n\n1. Clone this repository:\n\n   ```\n   git clone https://github.com/leescot/pubmed-mcp-smithery\n   cd pubmed-mcp-smithery\n   ```\n\n2. Install dependencies:\n   ```\n   pip install fastmcp requests\n   ```\n\n## Usage\n\n### Running locally\n\nStart the server:\n\n```\npython pubmed_enhanced_mcp_server.py\n```\n\nFor development mode with auto-reloading:\n\n```\nmcp dev pubmed_enhanced_mcp_server.py\n```\n\n### Adding to Claude Desktop\n\nEdit your Claude Desktop configuration file (_CLAUDE_DIRECTORY/claude_desktop_config.json_) to add the server:\n\n```json\n\"pubmed-enhanced\": {\n    \"command\": \"python\",\n    \"args\": [\n        \"/path/pubmed-mcp-smithery/pubmed_enhanced_mcp_server.py\"\n    ]\n}\n```\n\n## MCP Functions\n\nThe server provides these main functions:\n\n1. `search_pubmed` - Search PubMed for articles matching keywords with optional journal filtering\n\n   ```python\n   # Example\n   results = await search_pubmed(\n       keywords=[\"diabetes\", \"insulin resistance\"],\n       journal=\"Nature Medicine\",\n       num_results=5,\n       sort_by=\"date_desc\"\n   )\n   ```\n\n2. `get_mesh_terms` - Look up MeSH terms related to a medical concept\n\n   ```python\n   # Example\n   mesh_terms = await get_mesh_terms(\"diabetes\")\n   ```\n\n3. `get_pubmed_count` - Get the count of publications for multiple search terms\n\n   ```python\n   # Example\n   counts = await get_pubmed_count([\"diabetes\", \"obesity\", \"hypertension\"])\n   ```\n\n4. `format_paper_details` - Get detailed information about specific papers by PMID\n\n   ```python\n   # Example\n   paper_details = await format_paper_details([\"12345678\", \"87654321\"])\n   ```\n\n5. `pico_search` - Perform structured PICO (Population, Intervention, Comparison, Outcome) searches with synonyms\n   ```python\n   # Example\n   pico_results = await pico_search(\n       p_terms=[\"diabetes\", \"type 2 diabetes\", \"T2DM\"],\n       i_terms=[\"metformin\", \"glucophage\"],\n       c_terms=[\"sulfonylurea\", \"glipizide\"],\n       o_terms=[\"HbA1c reduction\", \"glycemic control\"]\n   )\n   ```\n\n## PICO Search Functionality\n\nThe PICO search tool helps researchers conduct evidence-based literature searches by:\n\n1. Allowing multiple synonym terms for each PICO element\n2. Combining terms within each element using OR operators\n3. Performing AND combinations between elements (P AND I, P AND I AND C, etc.)\n4. Returning both search queries and publication counts for each combination\n\nThis approach helps refine research questions and identify the most relevant literature.\n\n## Rate Limiting\n\nThe server implements automatic retry mechanism with backoff delays to handle potential rate limiting by NCBI's E-utilities service.\n\n## License\n\nThis project is licensed under the BSD 3-Clause License - see the LICENSE file for details.\n",
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