M3: MIMIC-IV + MCP + Models 🏥🤖

Query MIMIC-IV medical data using natural language through MCP clients

Transform medical data analysis with AI! Ask questions about MIMIC-IV data in plain English and get instant insights. Choose between local demo data (free) or full cloud dataset (BigQuery).

✨ Features

• 🔍 Natural Language Queries: Ask questions about MIMIC-IV data in plain English
• 🏠 Local SQLite: Fast queries on demo database (free, no setup)
• ☁️ BigQuery Support: Access full MIMIC-IV dataset on Google Cloud
• 🔒 Enterprise Security: OAuth2 authentication with JWT tokens and rate limiting
• 🛡️ SQL Injection Protection: Read-only queries with comprehensive validation

🚀 Quick Start

💡 Need more options? Run m3 --help to see all available commands and options.

📦 Installation

Choose your preferred installation method:

Option A: Install from PyPI (Recommended)

Step 1: Create Virtual Environment

# Create virtual environment (recommended)
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

Step 2: Install M3

# Install M3
pip install m3-mcp

Option B: Docker

# Clone repo first
git clone https://github.com/rafiattrach/m3.git && cd m3

# SQLite (demo DB)
docker build -t m3:lite --target lite .
docker run -d --name m3-server m3:lite tail -f /dev/null

# BigQuery (full dataset - requires GCP credentials)
docker build -t m3:bigquery --target bigquery .
docker run -d --name m3-server \
  -e M3_BACKEND=bigquery \
  -e M3_PROJECT_ID=YOUR_PROJECT_ID \
  -v $HOME/.config/gcloud:/root/.config/gcloud:ro \
  m3:bigquery tail -f /dev/null

MCP client config (Claude Desktop, LM Studio, etc.):

{
  "mcpServers": {
    "m3": {
      "command": "docker",
      "args": ["exec", "-i", "m3-server", "python", "-m", "m3.mcp_server"]
    }
  }
}

Stop container: docker stop m3-server && docker rm m3-server

Option C: Install from Source

Using standard pip

Step 1: Clone and Navigate

# Clone the repository
git clone https://github.com/rafiattrach/m3.git
cd m3

Step 2: Create Virtual Environment

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

Step 3: Install M3

# Install M3
pip install .

Using UV (Recommended)

Assuming you have UV installed.

Step 1: Clone and Navigate

# Clone the repository
git clone https://github.com/rafiattrach/m3.git
cd m3

Step 2: Create UV Virtual Environment

# Create virtual environment
uv venv

Step 3: Install M3

uv sync
# Do not forget to use `uv run` to any subsequent commands to ensure you're using the `uv` virtual environment

🗄️ Database Configuration

After installation, choose your data source:

Option A: Local Demo Database (Recommended for Beginners)

Perfect for learning and development - completely free!

1. Download demo database:

   m3 init mimic-iv-demo

2. Setup MCP Client:

   m3 config

   Alternative: For Claude Desktop specifically:

   m3 config claude

3. Restart your MCP client and ask:

   • "What tools do you have for MIMIC-IV data?"
   • "Show me patient demographics from the ICU"

Option B: BigQuery (Full Dataset)

For researchers needing complete MIMIC-IV data

Prerequisites

• Google Cloud account and project with billing enabled
• Access to MIMIC-IV on BigQuery (requires PhysioNet credentialing)

Setup Steps

1. Install Google Cloud CLI:

   macOS (with Homebrew):

   brew install google-cloud-sdk

   Windows: Download from https://cloud.google.com/sdk/docs/install

   Linux:

   curl https://sdk.cloud.google.com | bash

2. Authenticate:

   gcloud auth application-default login

   This will open your browser - choose the Google account that has access to your BigQuery project with MIMIC-IV data.

3. Setup MCP Client for BigQuery:

   m3 config

   Alternative: For Claude Desktop specifically:

   m3 config claude --backend bigquery --project-id YOUR_PROJECT_ID

4. Test BigQuery Access - Restart your MCP client and ask:

   Use the get_race_distribution function to show me the top 5 races in MIMIC-IV admissions.
   

🔧 Advanced Configuration

Need to configure other MCP clients or customize settings? Use these commands:

Interactive Configuration (Universal)

m3 config

Generates configuration for any MCP client with step-by-step guidance.

Quick Configuration Examples

# Quick universal config with defaults
m3 config --quick

# Universal config with custom database
m3 config --quick --backend sqlite --db-path /path/to/database.db

# Save config to file for other MCP clients
m3 config --output my_config.json

🔐 OAuth2 Authentication (Optional)

For production deployments requiring secure access to medical data:

# Enable OAuth2 with Claude Desktop
m3 config claude --enable-oauth2 \
  --oauth2-issuer https://your-auth-provider.com \
  --oauth2-audience m3-api \
  --oauth2-scopes "read:mimic-data"

# Or configure interactively
m3 config  # Choose OAuth2 option during setup

Supported OAuth2 Providers:

• Auth0, Google Identity Platform, Microsoft Azure AD, Keycloak
• Any OAuth2/OpenID Connect compliant provider

Key Benefits:

• 🔒 JWT Token Validation: Industry-standard security
• 🎯 Scope-based Access: Fine-grained permissions
• 🛡️ Rate Limiting: Abuse protection
• 📊 Audit Logging: Security monitoring

📖 Complete OAuth2 Setup Guide: See docs/OAUTH2_AUTHENTICATION.md for detailed configuration, troubleshooting, and production deployment guidelines.

Backend Comparison

SQLite Backend (Default)

• ✅ Free: No cloud costs
• ✅ Fast: Local queries
• ✅ Easy: No authentication needed
• ❌ Limited: Demo dataset only (~1k records)

BigQuery Backend

• ✅ Complete: Full MIMIC-IV dataset (~500k admissions)
• ✅ Scalable: Google Cloud infrastructure
• ✅ Current: Latest MIMIC-IV version (3.1)
• ❌ Costs: BigQuery usage fees apply

🛠️ Available MCP Tools

When your MCP client processes questions, it uses these tools automatically:

• get_database_schema: List all available tables
• get_table_info: Get column info and sample data for a table
• execute_mimic_query: Execute SQL SELECT queries
• get_icu_stays: ICU stay information and length of stay data
• get_lab_results: Laboratory test results
• get_race_distribution: Patient race distribution

🧪 Example Prompts

Try asking your MCP client these questions:

Demographics & Statistics:

• Prompt: What is the race distribution in MIMIC-IV admissions?
• Prompt: Show me patient demographics for ICU stays
• Prompt: How many total admissions are in the database?

Clinical Data:

• Prompt: Find lab results for patient X
• Prompt: What lab tests are most commonly ordered?
• Prompt: Show me recent ICU admissions

Data Exploration:

• Prompt: What tables are available in the database?
• Prompt: What tools do you have for MIMIC-IV data?

🎩 Pro Tips

• Do you want to pre-approve the usage of all tools in Claude Desktop? Use the prompt below and then select Always Allow
  • Prompt: Can you please call all your tools in a logical sequence?

🔍 Troubleshooting

Common Issues

SQLite "Database not found" errors:

# Re-download demo database
m3 init mimic-iv-demo

MCP client server not starting:

1. Check your MCP client logs (for Claude Desktop: Help → View Logs)
2. Verify configuration file location and format
3. Restart your MCP client completely

OAuth2 Authentication Issues

"Missing OAuth2 access token" errors:

# Set your access token
export M3_OAUTH2_TOKEN="Bearer your-access-token-here"

"OAuth2 authentication failed" errors:

• Verify your token hasn't expired
• Check that required scopes are included in your token
• Ensure your OAuth2 provider configuration is correct

Rate limit exceeded:

• Wait for the rate limit window to reset
• Contact your administrator to adjust limits if needed

🔧 OAuth2 Troubleshooting: See OAUTH2_AUTHENTICATION.md for detailed OAuth2 troubleshooting and configuration guides.

BigQuery Issues

"Access Denied" errors:

• Ensure you have MIMIC-IV access on PhysioNet
• Verify your Google Cloud project has BigQuery API enabled
• Check that you're authenticated: gcloud auth list

"Dataset not found" errors:

• Confirm your project ID is correct
• Ensure you have access to physionet-data project

Authentication issues:

# Re-authenticate
gcloud auth application-default login

# Check current authentication
gcloud auth list

👩‍💻 For Developers

Development Setup

Option A: Standard pip Development Setup

Step 1: Clone and Navigate

# Clone the repository
git clone https://github.com/rafiattrach/m3.git
cd m3

Step 2: Create and Activate Virtual Environment

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

Step 3: Install Development Dependencies

# Install in development mode with dev dependencies
pip install -e ".[dev]"
# Install pre-commit hooks
pre-commit install

Option B: Development Setup with UV (Recommended)

Step 1: Clone and Navigate

# Clone the repository
git clone https://github.com/rafiattrach/m3.git
cd m3

Step 2: Create and Activate UV Virtual Environment

# Create virtual environment
uv venv

Step 3: Install Development Dependencies

# Install in development mode with dev dependencies (by default, UV runs in editable mode)
uv sync

# Install pre-commit hooks
uv run pre-commit install

# Do not forget to use `uv run` to any subsequent commands to ensure you're using the `uv` virtual environment

Testing

pytest  # All tests (includes OAuth2 and BigQuery mocks)
pytest tests/test_mcp_server.py -v  # MCP server tests
pytest tests/test_oauth2_auth.py -v  # OAuth2 authentication tests

Test BigQuery Locally

# Set environment variables
export M3_BACKEND=bigquery
export M3_PROJECT_ID=your-project-id
export GOOGLE_CLOUD_PROJECT=your-project-id

# Optional: Test with OAuth2 authentication
export M3_OAUTH2_ENABLED=true
export M3_OAUTH2_ISSUER_URL=https://your-provider.com
export M3_OAUTH2_AUDIENCE=m3-api
export M3_OAUTH2_TOKEN="Bearer your-test-token"

# Test MCP server
m3-mcp-server

🔮 Roadmap

• 🏠 Local Full Dataset: Complete MIMIC-IV locally (no cloud costs)
• 🔧 Advanced Tools: More specialized medical data functions
• 📊 Visualization: Built-in plotting and charting tools
• 🔐 Enhanced Security: Role-based access control, audit logging
• 🌐 Multi-tenant Support: Organization-level data isolation

🤝 Contributing

We welcome contributions! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Submit a pull request

Built with ❤️ for the medical AI community

Need help? Open an issue on GitHub or check our troubleshooting guide above.