Apple Health MCP Server

Apple Health MCP Server Skill

Skill by ara.so — MCP Skills collection.

Overview

Apple Health MCP Server is a Model Context Protocol (MCP) server built with FastMCP that enables natural language querying of Apple Health data. It parses Apple Health XML exports and loads them into DuckDB, Elasticsearch, or ClickHouse for efficient querying. The server exposes MCP tools that allow LLMs to analyze health records, generate statistics, and identify trends without requiring SQL knowledge.

Key capabilities:

• Parse and import Apple Health XML exports
• Natural language queries over health data
• Multiple backend support (DuckDB, Elasticsearch, ClickHouse)
• Structured data analysis and trend detection
• Docker-ready deployment

Installation

Prerequisites

• Python 3.10+
• Apple Health export XML file
• MCP-compatible client (Claude Desktop, Cursor, etc.)

Setup Steps

1. Clone the repository:

git clone https://github.com/the-momentum/apple-health-mcp-server.git
cd apple-health-mcp-server

2. Install dependencies:

pip install -e .

3. Configure environment variables:

Create a .env file:

# Required: Path to Apple Health export XML
APPLE_HEALTH_EXPORT_PATH=/path/to/apple_health_export/export.xml

# Storage backend (duckdb, elasticsearch, or clickhouse)
STORAGE_BACKEND=duckdb

# DuckDB configuration (default backend)
DUCKDB_PATH=./data/health.db

# Optional: Elasticsearch configuration
# ELASTICSEARCH_URL=http://localhost:9200
# ELASTICSEARCH_INDEX=health_records
# ELASTICSEARCH_API_KEY=${ELASTICSEARCH_API_KEY}

# Optional: ClickHouse configuration
# CLICKHOUSE_HOST=localhost
# CLICKHOUSE_PORT=9000
# CLICKHOUSE_DATABASE=health
# CLICKHOUSE_USER=${CLICKHOUSE_USER}
# CLICKHOUSE_PASSWORD=${CLICKHOUSE_PASSWORD}

# Logging
LOG_LEVEL=INFO

4. Import your Apple Health data:

python -m apple_health_mcp_server.import_data

MCP Client Configuration

Add to your MCP client configuration (e.g., Claude Desktop claude_desktop_config.json):

{
  "mcpServers": {
    "apple-health": {
      "command": "python",
      "args": ["-m", "apple_health_mcp_server"],
      "env": {
        "APPLE_HEALTH_EXPORT_PATH": "/path/to/export.xml",
        "STORAGE_BACKEND": "duckdb",
        "DUCKDB_PATH": "./data/health.db"
      }
    }
  }
}

Docker Installation

# Build the image
docker build -t apple-health-mcp-server .

# Run with volume mounts
docker run -v /path/to/export.xml:/data/export.xml \
  -v ./data:/app/data \
  -e APPLE_HEALTH_EXPORT_PATH=/data/export.xml \
  -e STORAGE_BACKEND=duckdb \
  -e DUCKDB_PATH=/app/data/health.db \
  apple-health-mcp-server

Available MCP Tools

The server exposes the following MCP tools:

1. analyze_structure

Analyzes the structure and available data types in your Apple Health export.

# Tool call example (invoked by LLM)
{
  "name": "analyze_structure",
  "arguments": {}
}

# Returns: Summary of record types, counts, and date ranges

2. search_records

Search health records using flexible filters.

# Tool call with filters
{
  "name": "search_records",
  "arguments": {
    "record_type": "HKQuantityTypeIdentifierStepCount",
    "start_date": "2024-01-01",
    "end_date": "2024-01-31",
    "limit": 100,
    "source_name": "iPhone"
  }
}

Parameters:

• record_type: Apple Health record type identifier
• start_date: ISO format date (YYYY-MM-DD)
• end_date: ISO format date (YYYY-MM-DD)
• limit: Maximum records to return (default: 100)
• source_name: Filter by data source device/app
• unit: Filter by measurement unit

3. get_records_by_type

Extract all records of a specific type.

{
  "name": "get_records_by_type",
  "arguments": {
    "record_type": "HKQuantityTypeIdentifierHeartRate",
    "limit": 500
  }
}

4. generate_statistics

Generate statistical summaries for a specific metric.

{
  "name": "generate_statistics",
  "arguments": {
    "record_type": "HKQuantityTypeIdentifierDistanceWalkingRunning",
    "start_date": "2024-06-01",
    "end_date": "2024-06-30",
    "aggregation": "daily"
  }
}

Aggregation options: daily, weekly, monthly

5. analyze_trends

Identify trends and patterns in health data.

{
  "name": "analyze_trends",
  "arguments": {
    "record_type": "HKQuantityTypeIdentifierStepCount",
    "time_period": "last_30_days",
    "trend_type": "moving_average"
  }
}

Common Usage Patterns

Pattern 1: Initial Data Exploration

# First, analyze what data is available
analyze_structure()

# Then search for specific metrics
search_records(
    record_type="HKQuantityTypeIdentifierStepCount",
    start_date="2024-01-01",
    end_date="2024-12-31"
)

Pattern 2: Monthly Activity Summary

# Get daily step statistics for a month
generate_statistics(
    record_type="HKQuantityTypeIdentifierStepCount",
    start_date="2024-06-01",
    end_date="2024-06-30",
    aggregation="daily"
)

# Analyze workout data for the same period
search_records(
    record_type="HKWorkoutTypeIdentifier",
    start_date="2024-06-01",
    end_date="2024-06-30"
)

Pattern 3: Heart Rate Analysis

# Get heart rate readings
get_records_by_type(
    record_type="HKQuantityTypeIdentifierHeartRate",
    limit=1000
)

# Analyze trends
analyze_trends(
    record_type="HKQuantityTypeIdentifierHeartRate",
    time_period="last_90_days",
    trend_type="moving_average"
)

Python API Usage

While primarily an MCP server, you can use the underlying modules directly:

from apple_health_mcp_server.parser import AppleHealthParser
from apple_health_mcp_server.storage.duckdb import DuckDBStorage

# Parse Apple Health XML
parser = AppleHealthParser("/path/to/export.xml")
records = parser.parse()

# Store in DuckDB
storage = DuckDBStorage("./health.db")
storage.import_records(records)

# Query directly
results = storage.query(
    record_type="HKQuantityTypeIdentifierStepCount",
    start_date="2024-01-01",
    end_date="2024-12-31"
)

Custom Query Execution

from apple_health_mcp_server.storage.duckdb import DuckDBStorage

storage = DuckDBStorage("./health.db")

# Execute raw SQL
sql = """
    SELECT 
        DATE(start_date) as date,
        SUM(CAST(value AS DOUBLE)) as total_steps
    FROM health_records
    WHERE type = 'HKQuantityTypeIdentifierStepCount'
    AND start_date >= '2024-01-01'
    GROUP BY DATE(start_date)
    ORDER BY date
"""
results = storage.execute_query(sql)

Common Health Record Types

Key Apple Health record type identifiers:

• Activity: HKQuantityTypeIdentifierStepCount, HKQuantityTypeIdentifierDistanceWalkingRunning
• Heart: HKQuantityTypeIdentifierHeartRate, HKQuantityTypeIdentifierRestingHeartRate
• Sleep: HKCategoryTypeIdentifierSleepAnalysis
• Workouts: HKWorkoutTypeIdentifier
• Body: HKQuantityTypeIdentifierBodyMass, HKQuantityTypeIdentifierHeight
• Vitals: HKQuantityTypeIdentifierBloodPressureSystolic, HKQuantityTypeIdentifierOxygenSaturation

Configuration Options

Storage Backends

DuckDB (default - local file):

STORAGE_BACKEND=duckdb
DUCKDB_PATH=./data/health.db

Elasticsearch (scalable search):

STORAGE_BACKEND=elasticsearch
ELASTICSEARCH_URL=http://localhost:9200
ELASTICSEARCH_INDEX=health_records
ELASTICSEARCH_API_KEY=${ELASTICSEARCH_API_KEY}

ClickHouse (analytics):

STORAGE_BACKEND=clickhouse
CLICKHOUSE_HOST=localhost
CLICKHOUSE_PORT=9000
CLICKHOUSE_DATABASE=health
CLICKHOUSE_USER=${CLICKHOUSE_USER}
CLICKHOUSE_PASSWORD=${CLICKHOUSE_PASSWORD}

Performance Tuning

# Batch size for imports
IMPORT_BATCH_SIZE=10000

# Query result limits
DEFAULT_QUERY_LIMIT=100
MAX_QUERY_LIMIT=10000

# Connection pooling
DB_POOL_SIZE=5

Troubleshooting

Issue: Import fails with "File not found"

Solution: Verify the XML path and ensure the export is unzipped:

# Check file exists
ls -lh "$APPLE_HEALTH_EXPORT_PATH"

# Export should be in: export_cda.xml or export.xml
unzip export.zip -d ./export
export APPLE_HEALTH_EXPORT_PATH=./export/apple_health_export/export.xml

Issue: MCP server not connecting

Solution: Check MCP client logs and verify configuration:

# Test server directly
python -m apple_health_mcp_server

# Check environment variables
env | grep APPLE_HEALTH

# Validate JSON configuration
cat ~/.config/Claude/claude_desktop_config.json | jq .

Issue: Slow queries on large datasets

Solution: Switch to Elasticsearch or ClickHouse for better performance:

# Use ClickHouse for analytics workloads
STORAGE_BACKEND=clickhouse

# Or Elasticsearch for full-text search
STORAGE_BACKEND=elasticsearch

# Index optimization
CREATE INDEX idx_type_date ON health_records(type, start_date);

Issue: Memory errors during import

Solution: Reduce batch size and use streaming:

# Reduce batch size
IMPORT_BATCH_SIZE=5000

# Use Docker with memory limits
docker run --memory=2g apple-health-mcp-server

Issue: Missing data after import

Solution: Verify XML structure and check parser logs:

from apple_health_mcp_server.parser import AppleHealthParser

parser = AppleHealthParser("/path/to/export.xml")
parser.validate()  # Check for parsing errors

# Check what was imported
from apple_health_mcp_server.storage.duckdb import DuckDBStorage
storage = DuckDBStorage("./health.db")
print(storage.get_record_type_counts())

Issue: Date filtering not working

Solution: Ensure ISO 8601 date format (YYYY-MM-DD):

# Correct format
search_records(
    record_type="HKQuantityTypeIdentifierStepCount",
    start_date="2024-01-01",  # ISO format
    end_date="2024-12-31"
)

# Incorrect: "01/01/2024", "2024-1-1"

Advanced Usage

Custom Tool Extension

from apple_health_mcp_server.server import app
from fastmcp import Context

@app.tool()
async def custom_health_analysis(
    ctx: Context,
    metric: str,
    threshold: float
) -> str:
    """Custom analysis tool."""
    storage = ctx.app_context["storage"]
    
    results = storage.query(
        record_type=metric,
        filters={"value": {"$gte": threshold}}
    )
    
    return f"Found {len(results)} records above threshold"

Batch Data Export

from apple_health_mcp_server.storage.duckdb import DuckDBStorage
import pandas as pd

storage = DuckDBStorage("./health.db")

# Export to CSV
df = pd.DataFrame(storage.query(
    record_type="HKQuantityTypeIdentifierStepCount"
))
df.to_csv("steps_export.csv", index=False)

# Export to Parquet for efficient storage
df.to_parquet("steps_export.parquet")

Related Projects

• Open Wearables - Evolution of this project with continuous sync support
• FastMCP - Framework used to build this MCP server

Resources

• Apple Health XML Schema
• Model Context Protocol Specification
• Project Documentation