Ncbi MCP Server - MCP Server

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)

Create and initialize project
```
mkdir ncbi-mcp-server &amp;&amp; cd ncbi-mcp-server
poetry init
```
During init, add dependencies: mcp, httpx, typing-extensions

Create project structure

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

Install dependencies
```
poetry install
```

Test the server

poetry run python src/ncbi_mcp_server/server.py

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:
```
{
  "mcpServers": {
    "ncbi-literature": {
      "command": "poetry",
      "args": ["run", "python", "src/ncbi_mcp_server/server.py"],
      "cwd": "/FULL/PATH/TO/YOUR/ncbi-mcp-server"
    }
  }
}
```
Restart Claude Desktop and start searching!

Alternative Setup Methods

Click to expand alternative installation methods

Conda Environment

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

Standard pip + venv

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

Configure credentials:

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

Choose deployment method:

# 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:

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

# Replace the line: ncbi_client = NCBIClient()
# With:
ncbi_client = NCBIClient(
    email="[email protected]",
    api_key="your_api_key_here"
)

Rate Limits

Without API key: 3 requests/second
With API key: 10 requests/second
With API key + email: Higher limits for bulk requests

Development Workflow

Poetry Commands

poetry shell              # Activate virtual environment
poetry add package        # Add new dependency
poetry remove package     # Remove dependency
poetry update            # Update all dependencies
poetry run python ...    # Run commands in environment
poetry build             # Create distribution packages

Code Quality (if you added dev dependencies)

poetry add --group dev black mypy pytest isort flake8
poetry run black .       # Format code
poetry run mypy .        # Type checking  
poetry run pytest       # Run tests
poetry run isort .       # Sort imports

# They just need:
git clone your-repo
cd ncbi-mcp-server  
poetry install
# Everything works identically!

Field Tags for Advanced Searches

PubMed supports many field tags for precise searching:

[ti] - Title
[tiab] - Title and Abstract
[au] - Author
[mh] - MeSH Terms
[journal] - Journal Name
[pdat] - Publication Date
[pt] - Publication Type
[lang] - Language
[sb] - Subset (e.g., medline, pubmed)

Example Advanced Queries:

"machine learning"[ti] AND "phylogen*"[tiab] AND "2020"[pdat]:"2024"[pdat]
evolutionary[mh] AND computational[ti] AND (genomics[tiab] OR proteomics[tiab])
"ancient DNA"[ti] AND (paleogenomics[mh] OR phylogenomics[tiab])

Research Workflow Examples

Literature Review Workflow

Start broad: search_pubmed("computational phylogenetics")
Refine with MeSH: search_mesh_terms("phylogenetics")
Find key papers: Use publication dates and journal filters
Explore connections: get_related_articles(pmid="key_paper_id")
Deep dive: get_article_details(pmids=["12345", "67890"])

Staying Current

Recent methods: search_pubmed("new methods", date_range="90")
Follow key authors: search_pubmed("author_name[au]", sort="pub_date")
Track specific topics: advanced_search with your research keywords

Method Discovery

Algorithm papers: search_pubmed("algorithm[ti] AND your_field")
Software tools: search_pubmed("software[ti] OR tool[ti] AND bioinformatics")
Benchmarking: search_pubmed("comparison[ti] OR benchmark[ti]")

Troubleshooting

Common Issues

Server won't start:

Check Python version (3.8+ required)
Install dependencies: pip install -r requirements.txt
Verify file permissions

No search results:

Check query syntax (use proper field tags)
Try broader search terms
Verify internet connection

Rate limit errors:

Add delays between requests
Get NCBI API key for higher limits
Consider searching fewer results per query

XML parsing errors:

Usually temporary NCBI server issues
Retry after a few seconds
Check NCBI status: https://www.ncbi.nlm.nih.gov/

Getting Help

NCBI E-utilities documentation: https://www.ncbi.nlm.nih.gov/books/NBK25499/
PubMed search tips: https://pubmed.ncbi.nlm.nih.gov/help/
MeSH database: https://www.ncbi.nlm.nih.gov/mesh/

Contributing

This MCP server is designed to grow with the research community. Ideas for enhancement:

Additional databases: PMC, BioRxiv, databases beyond NCBI
Citation analysis: Track paper impact and citation networks
Export formats: BibTeX, EndNote, RIS for reference managers
Saved searches: Persistent search profiles and alerts
Full-text integration: When available through PMC

License

This project is open source. Feel free to modify and distribute according to your institution's policies.

Perfect for researchers in:

Evolutionary Biology & Phylogenetics
Computational Biology & Bioinformatics
Molecular Evolution & Population Genetics
Comparative Genomics & Proteomics
Systems Biology & Network Analysis
Biostatistics & Mathematical Biology
Ancient DNA & Paleogenomics
Conservation Genetics & Ecology

Start exploring the vast world of biological literature with powerful, precise searches!

NCBI Literature Search MCP Server

Features

Quick Start

Prerequisites

Setup (5 minutes)

Alternative Setup Methods

Conda Environment

Standard pip + venv

Usage Examples

For Evolutionary Biology Research

For Computational Biology Research

Advanced Search Examples

Tool Reference

search_pubmed

get_article_details

search_mesh_terms

get_related_articles

advanced_search

Analytics & Performance Monitoring

Analytics Tools

get_analytics_summary

get_detailed_metrics

reset_analytics

What's Tracked

Deployment

Quick Start

Deployment Options

1. Local Development

2. Docker Deployment

3. Production Deployment

Monitoring

Configuration

NCBI API Key (Optional but Recommended)

Rate Limits

Development Workflow

Poetry Commands

Code Quality (if you added dev dependencies)

Sharing with Colleagues

Field Tags for Advanced Searches

Research Workflow Examples

Literature Review Workflow

Staying Current

Method Discovery

Troubleshooting

Common Issues

Getting Help

Contributing

License

`search_pubmed`

`get_article_details`

`search_mesh_terms`

`get_related_articles`

`advanced_search`

`get_analytics_summary`

`get_detailed_metrics`

`reset_analytics`