Provides tools to query personal health data stored in a SQLite database, allowing AI agents to perform aggregated health summaries and execute raw SQL queries for detailed data analysis.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Wellness PlannerGenerate an energy-aware schedule for my tasks today."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Wellness Planner
A local MCP agent that queries personal health data and provides energy-aware task scheduling.
Data Note
This project uses simulated data. Health data is seeded from data/seed_db.py into a SQLite database. There is no Apple Health integration — real health data is not imported or synced.
Running the Code
Prerequisites
Python 3.14+
uv for dependency management
Standalone Agent (no MCP server required)
Run the Plan-and-Execute agent loop locally:
Uses yesterday's date if no date is given.
Reads from
data/health.dbanddata/todo.json.Prints a daily brief: sleep, activity, heart rate, readiness score, and proposed schedule.
MCP Server (for Cursor)
The MCP server is spawned by Cursor when needed — you do not start it manually in a separate terminal.
Configure Cursor to use the local MCP server (e.g.
.cursor/mcp.json):
Replace
/path/to/wellness_plannerwith your actual project path.Cursor will spawn the server and communicate over stdio.
Other Commands
Command | Purpose |
| Placeholder entry point |
| Seed |
MCP Tools
When the server is connected, these tools are available:
get_health_summary— Aggregated sleep, activity, and heart rate for a datecalculate_readiness_score— 1–10 readiness score for task timingquery_raw_logs— Run read-only SQL against the health DBget_tasks— Load tasks fromtodo.jsonpropose_schedule— Energy-aware schedule based on readiness and tasks