NCBI Literature Search MCP Server

A Model Context Protocol (MCP) server for searching NCBI databases, designed for researchers across all life sciences and biomedical fields. This server provides seamless access to PubMed's vast collection of 35+ million scientific articles through natural language queries, enabling AI assistants to help with literature reviews, research discovery, and scientific analysis.

Features

🔬 Comprehensive Search: Search PubMed's 35+ million articles across all biological disciplines 📊 Advanced Queries: Support for complex searches with boolean operators, field tags, and filters
🧬 Life Sciences Research: Covers all biological and biomedical fields including genetics, ecology, medicine, and biotechnology 💻 Computational Biology: Perfect for finding bioinformatics methods, algorithms, and computational tools 🔬 Research Applications: Literature reviews, hypothesis generation, method discovery, and staying current with scientific advances 📚 Full Article Details: Get abstracts, author lists, MeSH terms, DOIs, and publication information 🔗 Related Articles: Discover relevant research through NCBI's relationship algorithms 📖 MeSH Integration: Search and utilize Medical Subject Headings for precise terminology

Quick Start

Prerequisites

• Python 3.8 or higher
• Poetry (recommended) - Install Poetry

Setup (5 minutes)

1. Create and initialize project

   bash

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   mkdir ncbi-mcp-server && cd ncbi-mcp-server
   poetry init
   

   During init, add dependencies: mcp, httpx, typing-extensions

2. Create project structure

   bash

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   mkdir -p src/ncbi_mcp_server
   # Save server.py code as src/ncbi_mcp_server/server.py
   

3. Install dependencies

   bash

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   poetry install
   

4. Test the server

   bash

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   poetry run python src/ncbi_mcp_server/server.py
   

5. Configure Claude Desktop

   Edit your Claude Desktop config file:

   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%/Claude/claude_desktop_config.json
   • Linux: ~/.config/claude/claude_desktop_config.json

   Add this configuration:

   config.json

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   {
     "mcpServers": {
       "ncbi-literature": {
         "command": "poetry",
         "args": ["run", "python", "src/ncbi_mcp_server/server.py"],
         "cwd": "/FULL/PATH/TO/YOUR/ncbi-mcp-server"
       }
     }
   }
   

6. Restart Claude Desktop and start searching!

Alternative Setup Methods

conda env create -f environment.yml
conda activate ncbi-mcp
python server.py

python -m venv venv
source venv/bin/activate  # Linux/macOS
pip install -r requirements.txt
python server.py

Usage Examples

For Evolutionary Biology Research

Search for phylogenetic studies:

"Search for recent phylogenetic analysis papers on mammalian evolution"
→ Uses: search_pubmed with query "phylogenetic analysis[ti] AND mammalian[ti] AND evolution"

Find computational phylogenetics methods:

"Find papers about maximum likelihood methods for phylogenetic reconstruction"
→ Uses: search_pubmed with query "maximum likelihood[ti] AND phylogenetic reconstruction"

Search by specific organism:

"Find recent papers on Drosophila comparative genomics"
→ Uses: search_pubmed with query "Drosophila[ti] AND comparative genomics[ti]"

For Computational Biology Research

Algorithm and method papers:

"Search for machine learning applications in genomics from the last 2 years"
→ Uses: search_pubmed with date_range="730" and query "machine learning AND genomics"

Software and database papers:

"Find papers about new bioinformatics tools for sequence analysis"
→ Uses: search_pubmed with query "bioinformatics[ti] AND software[ti] AND sequence analysis"

Advanced Search Examples

Multi-criteria search:

"Find review articles about CRISPR applications in evolutionary studies published in Nature or Science"
→ Uses: advanced_search with terms=["CRISPR", "evolution"], publication_types=["Review"], journals=["Nature", "Science"]

Author-specific searches:

"Find recent papers by researchers working on ancient DNA and phylogenomics"
→ Uses: search_pubmed with query "ancient DNA[ti] AND phylogenomics[ti]"

Tool Reference

search_pubmed

Primary search tool for PubMed database

• query: Search terms (supports field tags like [ti] for title, [au] for author, [mh] for MeSH terms)
• max_results: Number of results (1-100, default: 20)
• sort: Sort by "relevance", "pub_date", "author", or "journal"
• date_range: Limit to recent articles ("30", "90", "365", "1095" days)

Examples:

• "CRISPR[ti] AND evolution" - CRISPR in title AND evolution anywhere
• "phylogenetic analysis[mh]" - Using MeSH term for phylogenetic analysis
• "computational biology AND machine learning" - Boolean search

get_article_details

Fetch complete information for specific articles

• pmids: List of PubMed IDs (up to 50)

Returns full abstracts, author lists, MeSH terms, DOI, publication details

search_mesh_terms

Find standardized Medical Subject Headings

• term: Term to search in MeSH database

Helps discover related concepts and improve search precision

get_related_articles

Discover articles related to a specific paper

• pmid: PubMed ID of reference article
• max_results: Number of related articles (1-50, default: 10)

Perfect for literature reviews and finding relevant research

advanced_search

Complex searches with multiple criteria

• terms: List of search terms to combine
• operator: "AND", "OR", or "NOT" to combine terms
• authors: List of author names
• journals: List of journal names
• publication_types: "Research Article", "Review", "Meta-Analysis", etc.
• date_from/date_to: Date range in YYYY/MM/DD format
• max_results: Number of results (1-100, default: 20)

Analytics & Performance Monitoring

The NCBI MCP Server includes comprehensive analytics to help you understand your research patterns and optimize performance.

Analytics Tools

get_analytics_summary

Get comprehensive analytics overview

"Show me my research analytics summary"

Returns:

• Total requests and uptime
• Operation breakdown (searches, fetches, etc.)
• Cache performance metrics
• Recent activity and error rates
• System health indicators

get_detailed_metrics

Detailed performance metrics for specific time periods

"Get detailed metrics for the last 24 hours"

• hours: Time period to analyze (default: 24)
• Operation-specific performance data
• Timeline analysis with hourly breakdowns
• Error rates and response times per operation

reset_analytics

Reset analytics data (use with caution)

"Reset all analytics data"

Note: This permanently clears all collected metrics.

What's Tracked

Usage Patterns:

• Search queries and frequency
• Most used operations
• Unique vs. repeated queries
• Peak usage periods

Performance Metrics:

• Response times for each operation
• Cache hit/miss rates
• Error rates and types
• Rate limiting efficiency

Research Insights:

• Popular search terms and patterns
• Research workflow analysis
• Literature access patterns
• Most accessed journals and topics

Deployment

Quick Start

1. Configure credentials:

   bash

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   cp .env.example .env
   # Edit .env with your NCBI email and API key
   

2. Choose deployment method:

   bash

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   # Local development
   ./deploy.sh local
   
   # Docker deployment
   ./deploy.sh docker
   
   # Production deployment
   ./deploy.sh production
   

Deployment Options

1. Local Development

Perfect for development and testing:

poetry install
poetry run python -m src.ncbi_mcp_server.server

2. Docker Deployment

Recommended for most users with two options:

Full setup with Redis (recommended):

# Copy and configure environment
cp .env.example .env
# Edit .env with your NCBI email and API key

# Start all services
docker-compose up -d

Simple setup without Redis:

# For basic usage without Redis dependencies
cp .env.example .env
# Edit .env with your NCBI email

docker-compose -f docker-compose.simple.yml up -d

Full setup includes:

• NCBI MCP Server container
• Redis cache for performance
• Redis Commander UI (http://localhost:8081)

Simple setup includes:

• NCBI MCP Server container only
• In-memory caching (no persistence)

3. Production Deployment

For production environments:

# Configure production settings
cp .env.production .env
# Edit with production values

# Deploy
./deploy.sh production

Monitoring

Docker logs:

docker-compose logs -f ncbi-mcp-server

Cache monitoring:

• Redis Commander: http://localhost:8081
• Cache stats via MCP tool: cache_stats()

Health checks:

# Test server health
curl http://localhost:8000/health

# Test via MCP
python -c "from src.ncbi_mcp_server.server import cache_stats; import asyncio; print(asyncio.run(cache_stats()))"

Configuration

NCBI API Key (Optional but Recommended)

For higher rate limits and better performance:

1. Register at NCBI: https://www.ncbi.nlm.nih.gov/account/
2. Get API key: https://www.ncbi.nlm.nih.gov/account/settings/
3. Add to server code in src/ncbi_mcp_server/server.py:

# Replace the line: ncbi_client = NCBIClient()
# With:
ncbi_client = NCBIClient(
    email="your.email@university.edu",
    api_key="your_api_key_here"
)