mcp-server-dexcom-health

MCP server for Dexcom CGM glucose data. Enables AI agents to access and analyze continuous glucose monitor data for health intelligence applications.

Features

• Real-time glucose monitoring - Current readings with trend analysis

• Historical data access - Up to 24 hours of glucose history

• Time window queries - Query specific time ranges (e.g., "4-3 hours ago")

• Clinical analytics - Time-in-range, GMI, CV%, AGP reports

• Episode detection - Automatic hypo/hyper event identification with detailed context

• Time-block analysis - Identify patterns by time of day

• Persistence layer support - Pass external data for long-term analysis

Related MCP server: Dexcom MCP Server

Tools

Installation

# Using uvx (recommended)
uvx mcp-server-dexcom-health

# Using pip
pip install mcp-server-dexcom-health

Configuration

Set environment variables:

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "dexcom": {
      "command": "uvx",
      "args": ["mcp-server-dexcom-health"],
      "env": {
        "DEXCOM_USERNAME": "your_username",
        "DEXCOM_PASSWORD": "your_password",
        "DEXCOM_REGION": "us"
      }
    }
  }
}

Usage Examples

Basic usage with Claude

"What's my current glucose?"

"How was my overnight control?"

"Did I have any lows today?"

"Give me my statistics for the last 12 hours"

"What about the hour before that?" (follow-up queries work!)

Time Window Queries

Query specific time ranges using start_minutes and end_minutes:

# Last 3 hours (standard)
get_glucose_readings(minutes=180)

# Specific window: 4 hours ago to 3 hours ago
get_glucose_readings(start_minutes=240, end_minutes=180)

# Stats for 6-5 hours ago
get_statistics(start_minutes=360, end_minutes=300)

# Episodes between 8-4 hours ago
detect_episodes(start_minutes=480, end_minutes=240)

Supported tools: get_glucose_readings, get_statistics, detect_episodes, export_data

Persistence Layer Integration

Tools that analyze data accept an optional data parameter for external data sources:

# Pass your own historical data
result = get_statistics(
    data=[
        {"glucose_mg_dl": 120, "timestamp": "2024-01-15T08:00:00Z"},
        {"glucose_mg_dl": 135, "timestamp": "2024-01-15T08:05:00Z"},
        # ... more readings
    ]
)

This enables building long-term analytics by storing data externally and passing it back for analysis.

Requirements

• Python 3.10+

• Active Dexcom Share session (requires Dexcom mobile app with Share enabled)

• At least one follower configured in Dexcom Share

License

MIT