Food Tracker MCP

🚀 Food Tracker MCP

A Model Context Protocol (MCP) server for tracking food consumption, analyzing nutritional components, and managing dietary restrictions.

🚀 Quick Start

The Food Tracker MCP integrates the OpenFoodFacts database to provide a comprehensive food tracking system with the following features:

• Search for food products using barcodes or keywords.
• Analyze the nutritional information of food products.
• Create meal plans based on specific nutritional goals and dietary restrictions.
• Record dietary intake.
• Manage dietary restrictions and allergies.
• Check if products meet the user's restrictions.
• View food records and nutritional summaries.

📦 Installation

Prerequisites

• Python 3.8 or higher
• pip (Python package manager)

Setup

1. Clone the repository or download the food_tracker.py file:

# Option 1: Clone the repository (if available)
git clone https://github.com/yourusername/food-tracker-mcp.git
cd food-tracker-mcp

# Option 2: Create a new directory and save the file there
mkdir food-tracker-mcp
cd food-tracker-mcp
# Copy the food_tracker.py file into this directory

2. Create a virtual environment (recommended):

python -m venv venv

3. Activate the virtual environment and install the packages:

source venv/bin/activate  # Use `venv\Scripts\activate` on Windows
pip install -r requirements.txt

💻 Usage Examples

Running the Server

1. After ensuring all dependencies are installed, run the following command to start the MCP server:

python food_tracker.py

2. Visit http://localhost:5000 to view the documentation.

Integrating with Claude

1. Add the following code to food_tracker.py to integrate with Claude:

from claude_sdk import Claude

# Initialize the Claude client
claude = Claude()

# Call the Claude API where needed
response = claude.chat("Analyze the user's dietary data")

Provided Tools

Basic Usage

1. Get Product Information

Use the get_product_by_barcode function to get detailed information about a food product by barcode:

from food_tracker import get_product_by_barcode

product_info = get_product_by_barcode(barcode="3270190119357")
print(product_info)

2. Record Dietary Intake

Use the log_food_consumption function to record the user's dietary intake:

from food_tracker import log_food_consumption

log_food_consumption(
    user_id="user123",
    barcode="3270190119357",
    quantity=1,
    meal_type="breakfast"
)

3. Check Product Compatibility

Use the check_product_compatibility function to check if a food product meets the user's dietary restrictions:

from food_tracker import check_product_compatibility

compatibility = check_product_compatibility(
    user_id="user123",
    barcode="3270190119357"
)
print(compatibility)

4. Analyze Nutritional Information

Use the analyze_nutrition function to get a detailed nutritional analysis of a food product:

from food_tracker import analyze_nutrition

nutrition_info = analyze_nutrition(barcode="3270190119357")
print(nutrition_info)

5. View Dietary Records

Use the view_food_log function to view the user's dietary records and nutritional summaries:

from food_tracker import view_food_log

log_summary = view_food_log(user_id="user123")
print(log_summary)

🔧 Technical Details

Data Storage

The Food Tracker MCP stores data locally in JSON files:

• User profiles: ./data/users/{user_id}.json
• Dietary records: ./data/logs/{user_id}_{date}.json

Extending MCP

You can extend the MCP in the following ways:

1. Add more nutritional analysis features.
2. Implement more detailed meal plans and goals.
3. Add recipe suggestions based on available ingredients.
4. Create reports and visualization tools to display nutritional data.
5. Implement social features to share progress.