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NCBI Literature Search MCP Server

by vitorpavinato
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Python
Remote

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

Setup (5 minutes)

  1. Create and initialize project

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

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

  2. Create project structure

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

  3. Install dependencies

    poetry install

  4. Test the server

    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:

    { "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 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

  1. Configure credentials:

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

  2. 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:

  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" )

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

Sharing with Colleagues

# 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

  1. Start broad: search_pubmed("computational phylogenetics")

  2. Refine with MeSH: search_mesh_terms("phylogenetics")

  3. Find key papers: Use publication dates and journal filters

  4. Explore connections: get_related_articles(pmid="key_paper_id")

  5. Deep dive: get_article_details(pmids=["12345", "67890"])

Staying Current

  1. Recent methods: search_pubmed("new methods", date_range="90")

  2. Follow key authors: search_pubmed("author_name[au]", sort="pub_date")

  3. Track specific topics: advanced_search with your research keywords

Method Discovery

  1. Algorithm papers: search_pubmed("algorithm[ti] AND your_field")

  2. Software tools: search_pubmed("software[ti] OR tool[ti] AND bioinformatics")

  3. 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!

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