Quantified Self MCP

Your health data. Your AI. Your machine.

A local-first MCP server that gives AI agents access to your personal health data — with privacy, provenance, and evidence built in.

· Documentation

What is it?

Quantified Self MCP gives an AI agent a local, structured interface to your own health history.

Instead of another health dashboard, you can ask your AI questions like:

Why did my HRV change recently?

How has my sleep changed over the last 30 days?

What happened to my resting heart rate after my workouts increased?

What patterns do you see across my recent health data?

Which metrics changed the most this month?

The AI retrieves the relevant data through MCP and analyzes it.

The goal isn't another health dashboard.

It's a trustworthy interface between your health history and your AI.

Why is it different?

🔒 Local-first

Your health database runs locally in SQLite.

You control where your data goes.

You can use a completely local AI stack:

Health Data
     ↓
Local SQLite
     ↓
Quantified Self MCP
     ↓
MCP Agent
     ↓
Local LLM

Cloud models are also supported. In that configuration, the MCP database remains local, but data returned by MCP tools may be sent to the model provider.

🤖 AI-native

Built specifically for MCP-compatible AI agents, rather than another standalone health application.

🔎 Evidence & provenance

Analytics can be traced back to the underlying health data and its provenance.

The goal is not simply:

HRV ↓ 24%

but understanding:

What data produced this result?
Where did it come from?
What period was analyzed?
How strong is the evidence?

Concretely, every trend, baseline, anomaly, comparison, and correlation carries a coverage/confidence object alongside its numbers, so the AI can talk about the result and the evidence behind it in the same breath:

Without evidence:

Your HRV decreased 24% over the last 30 days.

With evidence:

Your HRV decreased 24% over the last 30 days — but HRV was only logged on 71% of those days, so treat this trend cautiously rather than as a settled pattern.

The second answer is what calculate_metric_trend (and every other analytics tool) is designed to make possible: the tool returns the 24% figure and a confidence: "moderate" / coverage_ratio: 0.71 alongside it, and each tool's own description tells the calling model to fold that into its answer instead of reporting the number as if it came from a complete series.

📊 Longitudinal

Analyze health history across days, weeks, months, and years:

trends
baselines
anomalies
period comparisons
correlations
recent changes
metric explanations

📥 Import existing data

Bring your existing health history into the local database.

Supported imports include:

CSV
Apple Health exports

See the import documentation.

What can your AI do?

Read

Health metrics
Metric history
Raw measurements
Workout sessions
Data provenance

Analyze

Baselines
Trends
Anomalies
Period comparisons
Correlations

Explain

Recent changes
Metric changes
Supporting evidence
Data provenance

The MCP currently exposes 18 tools across data access, measurements, workouts, analytics, and personal intelligence.

See the tool reference for the complete list.

Supported data

Currently supported metrics include:

👟 Steps
😴 Sleep
❤️ Heart rate
❤️ Resting heart rate
📈 HRV
⚖️ Weight
🏋️ Workout minutes
🙂 Mood
💧 Water

The data model is extensible, so you can keep only the metrics you actually use.

Quick start

Install

pip install quantified-self-mcp

Import your data

CSV:

quantified-self-init-db your-health-data.csv

Apple Health:

quantified-self-init-db export.xml

Connect an MCP client

Configure your preferred MCP-compatible client to run:

quantified-self-mcp

Client-specific setup guides are available in docs/clients/.

Ask your AI

How has my sleep changed over the last 30 days?

That's it.

Privacy

Health data is sensitive.

Quantified Self MCP is designed around local ownership:

Your database stays on your machine.
No proprietary health-data cloud is required.
You choose the AI model.
You can run the entire stack locally.
Private metrics can be excluded from AI access.

For example:

HEALTH_PRIVATE_FIELDS=weight_kg,mood

Private fields remain stored locally but are excluded from MCP read and analytical operations.

See SECURITY.md for security considerations.

Architecture

                  AI Agent
                     │
                 MCP Protocol
                     │
                     ▼
          ┌─────────────────────┐
          │ Quantified Self MCP │
          │                     │
          │  Data · Analytics   │
          │  Evidence           │
          └──────────┬──────────┘
                     │
                     ▼
              Local SQLite
                     │
                     ▼
              Your Health Data

The MCP server is the bridge between your health history and your AI.

Documentation

Detailed documentation lives outside the README:

Open source

Quantified Self MCP is open source and built for the wider MCP ecosystem.

Contributions are welcome — especially around:

health-data imports
analytics
evidence and data quality
privacy
MCP client integrations
documentation

See CONTRIBUTING.md.

License

MIT

Your health data. Your AI. Your machine.

Local data. Open protocol. AI-powered insight.