The End of Manual Scraping: Creating Self-Generating APIs from Any Source

As a developer obsessed with reverse engineering apps and websites, I found myself constantly battling the same frustrating workflow. Every time I'd discover an interesting API endpoint, I'd extract the cURL command, manually parse through massive JSON responses, and spend 30 minutes integrating only the data I actually needed into my existing tech stack.

That's when I decided to build something that would change everything: a cURL to API converter that automatically generates custom endpoints with only the data you want.

The Problem: Manual Scraping is a Time Vampire

Every reverse engineering session followed the same painful pattern:

• Discover an API endpoint through network inspection
• Copy the massive cURL command with all its headers and parameters
• Execute it manually and wade through hundreds of JSON fields
• Manually extract only the 3-4 fields I actually needed
• Write custom parsing logic for each new data source

The Vision: Self-Generating APIs That Understand Data

Instead of manually parsing JSON every time, what if I could:

1. Submit any cURL command to a service
2. Select only the fields I care about from the response
3. Get a custom API endpoint that returns exactly that data structure
4. Use it immediately in my applications

# Submit a cURL command
curl -X POST "http://localhost:8000/generate" -d '{
  "curl_command": "curl -X GET https://api.github.com/repos/microsoft/vscode/issues"
}'

# Select only what you need
curl -X POST "http://localhost:8000/create-endpoint" -d '{
  "selections": {
    "issue_title": "[].title",
    "author_name": "[].user.login",
    "issue_id": "[].id"
  }
}'

# Get your custom endpoint instantly
curl "http://localhost:8000/api/v1/custom/xyz789"

Building the cURL to API Converter

Step 1: Secure cURL Command Parsing

The first challenge was safely executing arbitrary cURL commands without creating security vulnerabilities. I built a CurlParser class that validates and sanitizes commands:

class CurlParser:
    DANGEROUS_FLAGS = ['-T', '-o', '-O', '--upload-file', '--output']
    
    def validate_curl_command(self, curl_command: str) -> bool:
        # Block dangerous file operations
        for flag in self.DANGEROUS_FLAGS:
            if flag in curl_command:
                raise ValueError(f"Dangerous flag {flag} not allowed")
        
        # Only allow HTTP/HTTPS URLs
        if not re.search(r'https?://', curl_command):
            raise ValueError("Only HTTP/HTTPS URLs allowed")
            
        return True

Step 2: The Smart Array Grouping Challenge

The biggest technical hurdle wasn't parsing cURL commands, it was handling the infinite variety of JSON response structures. Every API returns data differently:

• Simple objects with basic fields
• Arrays of objects with nested properties
• Double-nested arrays (jobs with multiple locations)
• Triple-nested arrays (departments with teams with members)
• Mixed data types and null values everywhere

The Breakthrough: Intelligent Data Extraction

I developed a DataExtractor class that understands JSON relationships and automatically groups related fields:

class DataExtractor:
    def extract_with_smart_grouping(self, data, selections):
        """Groups related array fields while preserving structure"""
        
        # Group selections by their array path
        grouped_selections = self._group_by_array_path(selections)
        
        # Process each group to maintain relationships
        for array_path, fields in grouped_selections.items():
            if array_path:  # This is an array extraction
                result = self._extract_array_group(data, array_path, fields)
            else:  # Simple field extraction
                result = self._extract_simple_fields(data, fields)
                
        return result
    
    def _detect_array_patterns(self, path):
        """Detect nested array patterns like items[].locations[].name"""
        return re.findall(r'([^[\]]+)\[\]', path)

Step 3: Real-World API Success Stories

GitHub API: Complex Array Structures

# Input: Select specific fields from GitHub issues
{
  "issue_title": "[].title",
  "author_name": "[].user.login", 
  "issue_id": "[].id",
  "labels": "[].labels[].name"
}

# Output: Automatically grouped and structured
{
  "issues": [
    {
      "title": "Fix memory leak in extension host",
      "login": "developer123",
      "id": 12345,
      "labels": ["bug", "memory"]
    }
  ]
}

Apple Jobs API: Double-Nested Arrays

The Apple Jobs API was the ultimate test case with its complex nested structure:

# Complex nested structure: jobs with multiple locations
{
  "job_title": "res.searchResults[].postingTitle",
  "job_id": "res.searchResults[].positionId",
  "location_name": "res.searchResults[].locations[].name",
  "location_country": "res.searchResults[].locations[].countryName"
}

# Result: Nested data intelligently inlined
{
  "res_searchResults": [
    {
      "postingTitle": "Software Engineer",
      "positionId": "12345",
      "location_name": ["Cupertino", "Austin"],
      "location_country": ["United States", "United States"]
    }
  ]
}

The Game Changing Results

Before: Manual Integration Nightmare

• 30 minutes per API endpoint integration
• Manual JSON parsing for every new data source
• Custom code for each different response structure
• Constant debugging of field extraction logic

After: Instant API Generation

• 2 minutes from cURL command to working endpoint
• Automatic handling of any JSON structure
• Zero custom parsing code required
• Bulletproof extraction that handles edge cases

Advanced Features That Make It Bulletproof

Unicode and International Support

{
  "🚀_field": "data.rocket_emoji",
  "测试_data": "chinese.test.field",
  "café_location": "venue.café_name"
}

High Performance Processing

• 260KB+ datasets processed in 3 milliseconds
• Triple nested arrays with 1000+ items: sub-second processing
• Memory-efficient handling of massive datasets
• Zero performance impact from Unicode field names

Building Production-Ready APIs

The Three-Step Workflow

# 1. Analyze any cURL command  
curl -X POST "/generate" -d '{"curl_command": "curl https://api.example.com/data"}'
# Returns: task_id and discovered data structure

# 2. Select your data fields
curl -X POST "/create-endpoint" -d '{
  "task_id": "abc123",
  "selections": {"title": "posts[].title", "author": "posts[].user.name"}
}'
# Returns: custom endpoint URL

# 3. Use your new API immediately  
curl "/api/v1/custom/xyz789"
# Returns: exactly the data you specified

Enterprise API Compatibility

The system has been tested against major APIs:

• GitHub API: Complex array structures with nested objects
• Apple Jobs API: Double-nested arrays with location data
• Microsoft Careers API: Enterprise nested properties
• Custom APIs: Every edge case imaginable

Security and Reliability Features

• Secure cURL parsing that blocks dangerous operations
• Request timeout protection (30 seconds)
• Comprehensive input validation and error handling
• Only HTTP/HTTPS URLs allowed for safety

The JSON Torture Test: Building Bulletproof Extraction

To ensure the system could handle any real-world scenario, I built a comprehensive torture test that throws every possible edge case at the extractor:

• Memory bombs (massive nested arrays)
• Unicode hell (every Unicode category)
• JSON structures with null values and missing fields
• Mixed data types and malformed-like structures
• Performance killers with maximum complexity

Result: 🛡️ 100% success rate on all torture tests. The system is genuinely bulletproof.

What's Next: The Future of API Integration

This project opened up exciting possibilities:

Technical Stack

The final implementation leverages:

• FastAPI for high performance API endpoints
• Python with advanced JSON processing capabilities
• curl cffi for reliable HTTP request execution
• Comprehensive testing suite with edge case coverage
• In memory storage optimized for rapid prototyping

Closing Thoughts: From Manual Labor to Magic

What started as frustration with repetitive reverse engineering work became a tool that fundamentally changed how I approach API integration. Instead of spending hours on data extraction, I can now focus on building actual features.

If you're tired of manual JSON parsing and want to supercharge your reverse engineering workflow, this approach could save you dozens of hours on your next project.

Happy automating! 🚀