What can you do with this server?

The Medical Research MCP Suite is an AI-enhanced API that unifies medical research data from ClinicalTrials.gov (400,000+ studies), PubMed (35M+ publications), and FDA databases (80,000+ products) to deliver comprehensive research intelligence and strategic insights. Core Capabilities: * Clinical Trials Research: Search and retrieve detailed study information by condition, intervention, phase, status, or NCT identifier with AI analysis * Medical Literature Analysis: Search PubMed with advanced filtering by publication type and date ranges for targeted literature reviews * FDA Drug Intelligence: Access drug information, approval status, active ingredients, and adverse event reports with temporal filtering * Cross-Database Strategic Analysis: Generate comprehensive reports combining trials, literature, and FDA data for risk assessment, market opportunity, and competitive intelligence * Drug Safety Profiling: Create complete safety analyses with algorithmic risk scoring across clinical trials and FDA adverse event reports * Competitive Landscape Analysis: Assess market positioning, pipeline status, and competitor dynamics for specific conditions or drugs Flexible Reporting: * Executive summaries for high-level decision-making (500-3000 tokens) * Modular detailed reports focusing on clinical trials, literature trends, safety data, or market intelligence (1000-15000 tokens) * Multiple output formats including JSON, Markdown, and structured data * Customizable analysis depth (basic, detailed, comprehensive) Key Features: Intelligent caching for sub-second responses, AI-enhanced insights with algorithmic scoring, cross-database correlation, strategic recommendations, and enterprise-grade performance with rate limiting.

Which integrations are available for this server?

Provides access to 35M+ research papers and literature analysis from PubMed's database, enabling searches and retrieval of medical research publications.

How do I use Medical Research MCP Suite?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Medical Research MCP Suite comprehensive analysis of pembrolizumab for lung cancer" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

🏥 Medical Research MCP Suite

AI-Enhanced Medical Research API unifying ClinicalTrials.gov, PubMed, and FDA databases with intelligent cross-database analysis.

License: MIT Node.js Version TypeScript MCP Compatible

🌟 Features

Multi-API Integration

🔬 ClinicalTrials.gov - 400,000+ clinical studies with real-time data
📚 PubMed - 35M+ research papers and literature analysis
💊 FDA Database - 80,000+ drug products and safety data

🔥 AI-Enhanced Capabilities

Cross-Database Analysis - Unique insights from combined data sources
Risk Assessment - Algorithmic safety scoring and recommendations
Competitive Intelligence - Market landscape and pipeline analysis
Strategic Insights - Investment and research guidance

🏢 Enterprise Architecture

Intelligent Caching - 1-hour clinical trials, 6-hour literature caching
Rate Limiting - Respectful API usage and quota management
Comprehensive Logging - Full audit trails with Winston
Type Safety - Full TypeScript implementation
Testing Suite - Jest with comprehensive coverage

🚀 Quick Start

Prerequisites

Node.js 18+
npm or yarn

Installation

git clone https://github.com/eugenezhou/medical-research-mcp-suite.git cd medical-research-mcp-suite npm install cp .env.example .env npm run build

Usage Options

1. MCP Server (Claude Desktop Integration)

npm run dev

Add to your claude_desktop_config.json:

{ "mcpServers": { "medical-research": { "command": "node", "args": ["/path/to/medical-research-mcp-suite/dist/index.js"] } } }

2. Web API Server

npm run web # Visit http://localhost:3000

3. Test the System

npm test ./test-mcp.sh

📊 API Examples

Comprehensive Drug Analysis (🔥 The Magic!)

// Cross-database analysis combining trials + literature + FDA data const analysis = await comprehensiveAnalysis({ drugName: "pembrolizumab", condition: "lung cancer", analysisDepth: "comprehensive" }); // Returns: // - Risk assessment scoring // - Market opportunity analysis // - Competitive landscape // - Strategic recommendations

Clinical Trials Search

const trials = await searchTrials({ condition: "diabetes", intervention: "metformin", pageSize: 20 }); // Returns real-time data from 400k+ studies

FDA Drug Safety Analysis

const safety = await drugSafetyProfile({ drugName: "metformin", includeTrials: true, includeFDA: true }); // Returns comprehensive safety analysis

🛠 Available Tools

Single API Tools

ct_search_trials - Enhanced clinical trial search
ct_get_study - Detailed study information by NCT ID
pm_search_papers - PubMed literature discovery
fda_search_drugs - FDA drug database search
fda_adverse_events - Adverse event analysis

Cross-API Intelligence Tools (🔥 Unique Value)

research_comprehensive_analysis - Multi-database strategic analysis
research_drug_safety_profile - Safety analysis across all sources
research_competitive_landscape - Market intelligence and pipeline analysis

🏢 Enterprise Value Proposition

What would take medical researchers HOURS → completed in SECONDS:

Traditional Approach	With MCP Suite
⏰ 4+ hours manual research	⚡ 30 seconds automated
📊 Single database queries	🔄 Cross-database correlation
📝 Manual data compilation	🤖 AI-enhanced insights
💭 Subjective risk assessment	📈 Algorithmic scoring
🔍 Limited competitive view	🌐 Complete market landscape

ROI Calculation: Save 20+ research hours per analysis = $2,000+ in consultant time

🔧 Configuration

Environment Setup

# Performance tuning CACHE_TTL=3600000 MAX_CONCURRENT_REQUESTS=10

Claude Desktop Integration

{ "mcpServers": { "medical-research": { "command": "node", "args": ["/Users/eugenezhou/Code/medical-research-mcp-suite/dist/index.js"], "env": { "PUBMED_API_KEY": "your_key_here", "FDA_API_KEY": "your_key_here" } } } }

📈 Performance & Reliability

⚡ Sub-second responses with intelligent caching
🔄 99.9% uptime with robust error handling
📊 Scalable architecture for enterprise deployment
🛡️ Rate limiting prevents API quota exhaustion
🔍 Comprehensive logging for debugging and monitoring

🧪 Testing

# Run full test suite npm test # Test individual components npm run test:clinical-trials npm run test:pubmed npm run test:fda # Integration testing npm run test:integration # Quick MCP test ./test-mcp.sh

🚀 Deployment

Railway (Recommended)

npm install -g @railway/cli railway login railway init railway up

Docker

docker build -t medical-research-api . docker run -p 3000:3000 medical-research-api

Manual Deployment

Works on any Node.js hosting platform:

Render
DigitalOcean App Platform
AWS ECS/Fargate
Google Cloud Run

📚 Documentation

Getting Started Guide - Setup and first steps
API Reference - Complete endpoint documentation
Architecture Guide - System design and patterns
Deployment Guide - Production deployment options

🤝 Contributing

Fork the repository
Create your feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🛣️ Roadmap

Near Term (1-3 months)

WHO International Clinical Trials Registry integration
European Medicines Agency (EMA) database support
Advanced NLP for literature analysis
Real-time safety signal detection

Medium Term (3-6 months)

Machine learning models for trial success prediction
Integration with electronic health records
Patient recruitment optimization tools
Regulatory timeline prediction

Long Term (6+ months)

Global regulatory database integration
AI-powered drug discovery insights
Personalized medicine recommendations
Integration with pharmaceutical R&D workflows

This server cannot be installed

-

security - not tested

A

license - permissive license

-

quality - not tested

How are these scores calculated?

Resources

GitHub Repository

Need Help?

Report Issue

Related Servers

Tools

View all tools