Simple SQL-based quantified self tracking system with prompt-driven AI tools.

• Captures quantified self data (workouts, food, sleep) from photos/text
• AI processes inputs through MCP tools that work with SQL database
• Evolves schema naturally - AI adds columns as needed
• Generates insights through SQL analysis

5 tools that let AI work with SQL database:

• list_tables - discover existing data
• create_table - make new tables
• add_column - evolve schemas
• insert_data - store extracted data
• query_data - analyze patterns

3 agents that handle the workflow:

• Input Agent - extracts data from photos/text
• Extraction Agent - uses MCP tools to store data
• Analysis Agent - generates insights with SQL

Simple tables with consistent patterns:

CREATE TABLE workouts (
  id UUID PRIMARY KEY,
  date TIMESTAMP NOT NULL,
  exercise TEXT NOT NULL,
  sets INTEGER,
  reps INTEGER,
  weight REAL,
  created_at TIMESTAMP DEFAULT NOW()
);

1. Setup Database: Follow docs/supabase-setup.md
2. Build MCP Server: See docs/mcp-server-implementation.md
3. Test Integration: Use docs/implementation-guide.md

Input: Photo of CrossFit whiteboard "21-15-9 Thrusters (95lbs), Pull-ups"

Processing:

1. Input Agent extracts: workout type, exercises, rep scheme, weights
2. Extraction Agent uses list_tables() → sees workouts table
3. Extraction Agent uses insert_data() → stores 6 rows (3 sets × 2 exercises)
4. Analysis Agent uses query_data() → finds patterns and progress

Output: Structured workout data + insights about progress

spreadsheet-mcp/
├── README.md
├── docs/
│   ├── README.md                    # Architecture overview
│   ├── supabase-setup.md           # Database setup
│   ├── mcp-server-implementation.md # MCP server code
│   ├── mcp-tool-examples.md        # Tool usage patterns
│   ├── n8n-agent-prompts.md        # Agent prompt templates
│   └── implementation-guide.md     # Step-by-step guide
└── sql/
    └── sample_tables.sql           # Database schema + sample data

Each MCP tool has rich prompt context that teaches the AI:

• When to create new tables vs extend existing ones
• How to handle schema evolution gracefully
• What constitutes good vs bad data modeling decisions

# User: "I want to track RPE now"
# AI automatically:
list_tables()  # → sees workouts table
add_column("workouts", {"name": "rpe", "type": "INTEGER"})  # → adds RPE column
insert_data("workouts", {..., "rpe": 8})  # → stores workout with RPE

-- Find workout performance on high-fiber days
SELECT w.exercise, w.weight, f.fiber
FROM workouts w
JOIN food f ON DATE(w.date) = DATE(f.date)  
WHERE f.fiber > 10;

Everything your engineer needs is in the docs/ folder:

• Complete MCP server implementation
• Supabase setup with metadata system
• n8n agent prompts for automation
• Step-by-step implementation guide

Time Estimate:

• Core MCP server: 2-3 hours
• Database setup: 30 minutes
• Basic testing: 1 hour
• n8n integration: 4-6 hours

The system is designed to be simple but powerful - normal SQL tables with AI tools that know how to use them intelligently.