What is Personal Mcp

Personal-MCP is a Model Context Protocol server designed for personal health and well-being tracking. It provides tools for monitoring workouts, nutrition, and daily journal entries, enhanced with AI-assisted analysis through Claude integration.

Use cases

Use cases include fitness enthusiasts tracking their workouts, individuals managing their diet and nutrition, and anyone looking to monitor their mood and energy levels through daily journaling.

How to use

To use Personal-MCP, install it via Smithery or pip, and run the server using the command ‘personal-mcp run’. For development purposes, you can use ‘personal-mcp dev’ for hot reloading. The MCP Inspector can be used for debugging.

Key features

Key features include workout tracking (logging exercises, tracking effort, and historical analysis), nutrition management (logging meals, tracking intake, and monitoring satisfaction), and a journal system (daily entries, mood tracking, and trend analysis).

Where to use

Personal-MCP can be used in personal health and fitness domains, including individual workout tracking, nutrition management, and mental well-being monitoring.

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.