iflow-mcp_evangstav_personal-mcp 0.1.0

Personal MCP server for health and well-being tracking

Personal MCP Server

A Model Context Protocol server for personal health and well-being tracking. This server provides tools and resources for tracking workouts, nutrition, and daily journal entries, with AI-assisted analysis through Claude integration.

Features

Workout Tracking

• Log exercises, sets, and reps
• Track perceived effort and post-workout feelings
• Calculate safe training weights with rehabilitation considerations
• Historical workout analysis
• Shoulder rehabilitation support
• RPE-based load management

Nutrition Management

• Log meals and individual food items
• Track protein and calorie intake
• Monitor hunger and satisfaction levels
• Daily nutrition targets and progress
• Pre/post workout nutrition tracking
• Meal timing analysis

Journal System

• Daily entries with mood and energy tracking
• Sleep quality and stress level monitoring
• Tag-based organization
• Trend analysis and insights
• Correlation analysis between workouts, nutrition, and well-being
• Pattern recognition in mood and energy levels

Installation

Installing via Smithery

To install Personal Health Tracker for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install personal-mcp --client claude

Prerequisites

• Python 3.10 or higher
• pip or uv package manager

Using pip

pip install -e .

Development Installation

git clone https://github.com/yourusername/personal-mcp.git
cd personal-mcp
uv pip install -e ".[dev]"

Usage

Basic Server

Run the server with default settings:

personal-mcp run

Development Mode

Run with hot reloading for development:

personal-mcp dev

MCP Inspector

Debug with the MCP Inspector:

personal-mcp inspect

Claude Desktop Integration

Install to Claude Desktop:

personal-mcp install --claude-desktop

Configuration Options

personal-mcp --help

Available options:

• --name: Set server name (default: "Personal Assistant")
• --db-path: Specify database location
• --dev: Enable development mode
• --inspect: Run with MCP Inspector
• -v, --verbose: Enable verbose logging

MCP Tools

Workout Tools

# Log a workout
workout = {
    "date": "2024-01-07",
    "exercises": [
        {
            "name": "Bench Press",
            "sets": [
                {"weight": 135, "reps": 10, "rpe": 7}
            ]
        }
    ],
    "perceived_effort": 8
}

# Calculate training weights
params = {
    "exercise": "Bench Press",
    "base_weight": 200,
    "days_since_surgery": 90,
    "recent_pain_level": 2,
    "recent_rpe": 7
}

Nutrition Tools

# Log a meal
meal = {
    "meal_type": "lunch",
    "foods": [
        {
            "name": "Chicken Breast",
            "amount": 200,
            "unit": "g",
            "protein": 46,
            "calories": 330
        }
    ],
    "hunger_level": 7,
    "satisfaction_level": 8
}

# Check nutrition targets
targets = await mcp.call_tool("check_nutrition_targets", {"date": "2024-01-07"})

Journal Tools

# Create a journal entry
entry = {
    "entry_type": "daily",
    "content": "Great workout today...",
    "mood": 8,
    "energy": 7,
    "sleep_quality": 8,
    "stress_level": 3,
    "tags": ["workout", "recovery"]
}

# Analyze entries
analysis = await mcp.call_tool("analyze_journal_entries", {
    "start_date": "2024-01-01",
    "end_date": "2024-01-07"
})

Development

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=personal_mcp

# Run specific test file
pytest tests/test_database.py

Code Quality

# Format code
black src/personal_mcp

# Lint code
ruff check src/personal_mcp

# Type checking
mypy src/personal_mcp

Project Structure

personal-mcp/
├── src/
│   └── personal_mcp/
│       ├── tools/
│       │   ├── workout.py
│       │   ├── nutrition.py
│       │   └── journal.py
│       ├── database.py
│       ├── models.py
│       ├── resources.py
│       ├── prompts.py
│       └── server.py
├── tests/
│   ├── test_database.py
│   ├── test_server.py
│   └── test_cli.py
├── pyproject.toml
└── mcp.json

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.