A comprehensive Model Context Protocol (MCP) server that provides access to PubMed, NCBI's database of biomedical literature with over 36 million citations from MEDLINE, life science journals, and online books.
Developed by Augmented Nature
The PubMed MCP server exposes the full NCBI E-utilities and PubMed Central APIs through 16 specialized MCP tools, allowing you to search, retrieve, and analyze biomedical literature directly from your MCP-enabled environment.
- search_articles - Search PubMed by keywords, authors, journals, dates, or MeSH terms with pagination
- advanced_search - Complex queries with field-specific searches and boolean operators (AND/OR)
- search_by_author - Find articles by specific author(s) with optional affiliation filtering
- search_by_journal - Search within specific journals with date range filtering
- search_by_mesh_terms - Search using Medical Subject Headings (MeSH) with major topic filtering
- get_trending_articles - Get recently published articles in a specific field (last 30-365 days)
- get_article_details - Get comprehensive metadata and abstract for a specific PMID
- get_abstract - Retrieve article abstract by PMID with basic metadata
- get_full_text - Retrieve full text from PubMed Central (PMC) when available
- batch_article_lookup - Retrieve multiple articles efficiently (up to 200 PMIDs)
- get_cited_by - Find articles that cite a specific PMID
- get_references - Get reference list for an article
- get_similar_articles - Find related articles based on content similarity
- export_citation - Export citations in various formats (APA, MLA, Chicago, BibTeX, RIS)
- validate_pmid - Validate PubMed ID format and check if article exists
- convert_identifiers - Convert between PMID, DOI, and PMC ID
- Real-time access to 36+ million biomedical citations
- Automatic rate limiting (3 req/s without API key, 10 req/s with key)
- Comprehensive error handling with retry logic
- Support for all PubMed search fields and operators
- MeSH term integration for precise medical queries
- Full-text access via PubMed Central when available
- Node.js 16+ (install from nodejs.org)
- Optional: NCBI API key for higher rate limits (get from NCBI Account Settings)
{
"mcpServers": {
"pubmed-server": {
"command": "node",
"args": ["path/to/pubmed-server/build/index.js"],
"autoApprove": [
"search_articles",
"get_article_details",
"get_abstract",
"validate_pmid"
]
}
}
}{
"mcpServers": {
"pubmed-server": {
"command": "node",
"args": ["path/to/pubmed-server/build/index.js"],
"env": {
"NCBI_API_KEY": "your_api_key_here",
"NCBI_EMAIL": "your_email@example.com"
},
"autoApprove": [
"search_articles",
"get_article_details",
"get_abstract",
"validate_pmid",
"search_by_author",
"search_by_journal"
]
}
}
}{
"query": "CRISPR gene editing",
"max_results": 10,
"sort": "pub_date"
}{
"pmid": "41138228"
}{
"title": "machine learning",
"author": "Smith J",
"mesh_terms": ["Artificial Intelligence", "Diagnosis"],
"boolean_operator": "AND",
"max_results": 50
}{
"author_name": "Smith J",
"affiliation": "Harvard Medical School",
"max_results": 50
}{
"journal_name": "Nature",
"keywords": "cancer immunotherapy",
"date_from": "2024/01/01",
"date_to": "2025/12/31",
"max_results": 25
}{
"mesh_terms": ["COVID-19", "Vaccines"],
"major_topic_only": true,
"max_results": 50
}{
"field": "artificial intelligence in medicine",
"days": 30,
"max_results": 20
}{
"pmcid": "PMC1234567"
}{
"pmids": ["41138228", "41137959", "41137488"]
}{
"pmid": "41138228",
"format": "bibtex"
}{
"pmid": "12345678",
"max_results": 100
}{
"identifier": "10.1080/15476286.2025.2577449",
"identifier_type": "doi"
}Common Parameters:
query- Search query (keywords, phrases, field tags)max_results- Number of results (1-1000, default: 20)start- Starting position for pagination (default: 0)sort- Sort order: relevance, pub_date, author, journal
Date Parameters:
date_from- Start date (YYYY/MM/DD format)date_to- End date (YYYY/MM/DD format)
Field Tags:
[Title]- Article title[Author]- Author name[Journal]- Journal name[MeSH Terms]- Medical Subject Headings[Abstract]- Abstract text[Affiliation]- Author affiliation[Publication Type]- Type of publication[DOI]- Digital Object Identifier
Example: "Smith J[Author] AND cancer[Title]"
Medical Subject Headings (MeSH) is the NLM controlled vocabulary thesaurus used for indexing articles. Using MeSH terms provides more precise search results than keyword searches.
Common MeSH Categories:
- Diseases
- Chemicals and Drugs
- Analytical, Diagnostic and Therapeutic Techniques
- Anatomy
- Organisms
- Psychiatry and Psychology
- Phenomena and Processes
The server supports multiple citation formats:
- APA - American Psychological Association
- MLA - Modern Language Association
- Chicago - Chicago Manual of Style
- BibTeX - LaTeX bibliography format
- RIS - Research Information Systems format
All tools return structured JSON data including:
- Article metadata (PMID, title, authors, journal, publication date)
- Abstract text (when available)
- MeSH terms and keywords
- DOI and other identifiers
- Citation information
- Full-text availability status
All PMIDs must be numeric strings (e.g., "12345678")
The server includes comprehensive error handling for:
- Network Issues: Automatic retry with exponential backoff
- Rate Limiting: Automatic throttling to comply with NCBI policies
- Invalid Parameters: Clear error messages for validation failures
- API Errors: Proper HTTP status code handling
- Missing Data: Graceful handling of unavailable fields
Common error responses include:
{
"error": "Invalid PMID format: abc123",
"isError": true
}# Install dependencies
npm install
# Build TypeScript to JavaScript
npm run build
# Development with watch mode
npm run watch@modelcontextprotocol/sdk- MCP server frameworkaxios- HTTP client for API requestsxml2js- XML parsing for PubMed responses- TypeScript for type safety and development
E-utilities API: https://eutils.ncbi.nlm.nih.gov/entrez/eutils/ PubMed Central API: https://www.ncbi.nlm.nih.gov/pmc/tools/developers/ Coverage: 36+ million citations from MEDLINE, life science journals, and online books Data Updates: Real-time access to the latest publications
- Without API key: 3 requests per second
- With API key: 10 requests per second
The server automatically handles rate limiting to comply with NCBI's usage policies.
-
Server Connection Issues:
# Test server manually cd pubmed-server node build/index.js # Should show: "PubMed MCP Server running on stdio"
-
Invalid PMID Errors:
- Ensure PMIDs are numeric strings
- Example: "12345678" ✓, "PMID12345678" ✗
-
No Results Returned:
- Check search query syntax
- Try broader search terms
- Verify field tags are correct
-
Rate Limit Errors:
- Consider obtaining an NCBI API key
- Reduce request frequency
- Use batch operations when possible
-
Full Text Not Available:
- Not all articles have full text in PMC
- Check if article has a PMC ID
- Some articles may be behind paywalls
- Literature Review: Comprehensive search across biomedical literature
- Citation Analysis: Track citations and references for research impact
- Systematic Reviews: Batch retrieval and analysis of multiple articles
- Meta-Analysis: Collect data from multiple studies efficiently
- Research Trends: Identify trending topics and recent publications
- Author Research: Find all publications by specific researchers
- Journal Analysis: Analyze publication patterns in specific journals
- MeSH-Based Discovery: Precise medical subject searches
This server provides access to publicly available biomedical literature from PubMed. Users should:
- Follow institutional policies for research and publication
- Respect copyright and licensing terms for full-text articles
- Cite sources appropriately in publications
- Use data for legitimate research and educational purposes
- Comply with NCBI's usage policies and rate limits
MIT License - See LICENSE file for details
Data Source: PubMed/NCBI (https://pubmed.ncbi.nlm.nih.gov) API Documentation: https://www.ncbi.nlm.nih.gov/books/NBK25501/ Server Version: 1.0.0