mcp-server-dexcom-health 0.1.1

MCP server for Dexcom CGM glucose data - continuous health intelligence for AI agents

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
• 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

Tools

Tool	Description
get_current_glucose	Current glucose reading with trend
get_glucose_readings	Historical readings (up to 24h)
get_statistics	TIR, CV%, GMI, and other metrics
get_status_summary	Complete "how am I doing?" summary
detect_episodes	Find hypo/hyper episodes
get_episode_details	Deep analysis of each episode
analyze_time_blocks	Patterns by time of day
check_alerts	Real-time threshold alerts
export_data	Export for external storage
get_agp_report	Clinical AGP report

Installation

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

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

Configuration

Set environment variables:

Variable	Required	Description
DEXCOM_USERNAME	Yes	Dexcom username, email, or phone [phone])
DEXCOM_PASSWORD	Yes	Dexcom password
DEXCOM_REGION	No	us (default), ous (outside US), or jp (Japan)

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"

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": "2024-01-15T08:00:00"},
        {"glucose_mg_dl": 135, "timestamp": "2024-01-15T08:05:00"},
        # ... 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