How to Parse Messy Recipe Text into Clean JSON (in Seconds)

Messy recipe text is a nightmare.

If you've ever tried to build a food or nutrition app, you've probably run into the same problem:

Humans can read that easily — but your code? Not so much. You end up fighting with regex, lookups, and inconsistent formatting.

That's why I built the Avocavo Nutrition API. It takes any ingredient list — no matter how messy — and returns clean, structured JSON in under 0.2 seconds.

Example: From messy text to perfect JSON

Input:

2 cups fresh spinach, 1 large salmon fillet (about 6 oz), 1 tbsp olive oil, 1/2 avocado sliced, 1 cup quinoa cooked

Output (shortened for clarity):

[
  {
    "name": "spinach, fresh",
    "amount": 2,
    "unit": "cup",
    "calories": 14.0,
    "protein_g": 1.8,
    "fat_g": 0.2,
    "carbs_g": 2.2,
    "fiber_g": 1.4,
    "source": "USDA:11457",
    "confidence": 0.95
  },
  {
    "name": "salmon, atlantic, farmed",
    "amount": 6,
    "unit": "oz",
    "calories": 412.0,
    "protein_g": 55.6,
    "fat_g": 18.4,
    "carbs_g": 0,
    "omega_3_mg": 2586,
    "source": "USDA:15236",
    "confidence": 0.93
  },
  {
    "name": "olive oil",
    "amount": 1,
    "unit": "tbsp",
    "calories": 119.0,
    "protein_g": 0,
    "fat_g": 13.5,
    "carbs_g": 0,
    "vitamin_e_mg": 1.94,
    "source": "USDA:04053",
    "confidence": 0.98
  }
]

You get full macros, micronutrients, and USDA FDC source references for every ingredient.

How to try it (free)

1. Step 1: Open the Playground
2. Step 2: Paste in your ingredient list and hit Run
3. Step 3: See structured JSON instantly

API Integration Example

JavaScript/Node.js

fetch("https://app.avocavo.app/api/v2/nutrition/recipe", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
    "X-API-Key": "YOUR_API_KEY"
  },
  body: JSON.stringify({
    "ingredients": [
      "2 cups fresh spinach",
      "1 large salmon fillet (about 6 oz)",
      "1 tbsp olive oil",
      "1/2 avocado sliced",
      "1 cup quinoa cooked"
    ],
    "servings": 1
  })
})
.then(res => res.json())
.then(data => {
  console.log('Nutrition data:', data);
  // Process your clean, structured data
});

Python

import requests

response = requests.post(
    'https://app.avocavo.app/api/v2/nutrition/recipe',
    headers={
        'X-API-Key': 'YOUR_API_KEY',
        'Content-Type': 'application/json'
    },
    json={
        "ingredients": [
            "2 cups fresh spinach",
            "1 large salmon fillet (about 6 oz)",
            "1 tbsp olive oil",
            "1/2 avocado sliced",
            "1 cup quinoa cooked"
        ],
        "servings": 1
    }
)

nutrition_data = response.json()
print(f"Total calories: {nutrition_data['total_calories']}")
print(f"Protein: {nutrition_data['total_protein_g']}g")

cURL

curl -X POST https://app.avocavo.app/api/v2/nutrition/recipe" \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "ingredients": [
      "2 cups fresh spinach",
      "1 large salmon fillet (about 6 oz)",
      "1 tbsp olive oil",
      "1/2 avocado sliced",
      "1 cup quinoa cooked"
    ],
    "servings": 1
  }'

Why developers love this API

Real-world use cases

• Recipe apps: Parse user-submitted recipes for nutrition estimates
• Meal planning: Calculate macro targets for custom meal plans
• Fitness tracking: Log food intake from natural language
• E-commerce: Add nutrition facts to food product listings
• Restaurant tech: Auto-generate nutrition data for menu items

What's included in the response