AI-Powered Proxy Systems: MCP Servers and the Future of Proxies in AI Development

# Modern Python example for AI-powered proxy integration
# Using async patterns and type hints for better IDE support
import asyncio
from typing import List, Dict, Any, Optional
import aiohttp
from pydantic import BaseModel

# Type definitions for strong typing
class ProxyConfig(BaseModel):
    target_site: str
    region: str
    task_type: str
    priority: str = "medium"
    fingerprint_profile: Optional[str] = None
    session_persistence: bool = False
    proxy_type_preference: Optional[str] = None
    
class CollectionResult(BaseModel):
    keyword: str
    region: str
    data: str
    metadata: Dict[str, Any]

# Modern MCP client with proper typing
class MCPProxyClient:
    def __init__(self, api_key: str, project_id: str, endpoint: str = "https://api.coronium.io/v2"):
        self.api_key = api_key
        self.project_id = project_id
        self.endpoint = endpoint
        self._headers = {
            "Authorization": f"Bearer {api_key}",
            "X-Project-ID": project_id,
            "Content-Type": "application/json"
        }
        
    async def create_session(self, config: ProxyConfig):
        """
        Create an intelligent proxy session based on the provided configuration.
        The AI system selects the optimal proxy based on historical performance and current conditions.
        """
        # Real implementation would call the MCP service API
        async with aiohttp.ClientSession(headers=self._headers) as session:
            # This would be a real API call in production
            async with session.request(
                method="POST",
                url=f"{self.endpoint}/sessions",
                json=config.dict()
            ) as response:
                response_data = await response.json()
                return MCPSession(self, session_id=response_data["session_id"])

class MCPSession:
    def __init__(self, client: MCPProxyClient, session_id: str):
        self.client = client
        self.session_id = session_id
        
    async def __aenter__(self):
        # In a real implementation, this might activate the session
        return self
        
    async def __aexit__(self, exc_type, exc_val, exc_tb):
        # In a real implementation, this would close and report session metrics
        pass
        
    async def get(self, url: str, headers: Optional[Dict[str, str]] = None) -> aiohttp.ClientResponse:
        """Make a GET request through the intelligently selected proxy"""
        # Real implementation would route through the MCP proxy network
        # This is simplified for the example
        async with aiohttp.ClientSession() as session:
            return await session.get(
                url,
                headers={
                    "X-MCP-Session-ID": self.session_id,
                    **(headers or {})
                }
            )

# Data collection with proper error handling and concurrency management
async def collect_training_data(
    keywords: List[str], 
    regions: List[str],
    concurrency_limit: int = 5
) -> List[CollectionResult]:
    """Collect training data across multiple keywords and regions with controlled concurrency"""
    proxy_client = MCPProxyClient(
        api_key="your_api_key",
        project_id="ai_training_project"
    )
    
    results: List[CollectionResult] = []
    semaphore = asyncio.Semaphore(concurrency_limit)
    
    async def process_item(keyword: str, region: str):
        async with semaphore:
            try:
                # The intelligent proxy selection happens here
                async with await proxy_client.create_session(
                    ProxyConfig(
                        target_site="example.com",
                        region=region,
                        task_type="data_collection",
                        priority="high",
                        fingerprint_profile="standard_browser"
                    )
                ) as session:
                    response = await session.get(
                        f"https://example.com/search?q={keyword}"
                    )
                    
                    if response.status == 200:
                        text_data = await response.text()
                        proxy_metadata = response.headers.get("X-MCP-Metadata", "{}")
                        
                        results.append(CollectionResult(
                            keyword=keyword,
                            region=region,
                            data=text_data,
                            metadata=proxy_metadata
                        ))
            except Exception as e:
                print(f"Error collecting {keyword} in {region}: {str(e)}")
    
    # Create tasks for all combinations
    tasks = []
    for keyword in keywords:
        for region in regions:
            tasks.append(process_item(keyword, region))
    
    # Run all tasks concurrently with controlled parallelism
    await asyncio.gather(*tasks)
    return results

# Modern async execution with proper logging and error handling
async def main():
    keywords = ["machine learning", "artificial intelligence", "neural networks"]
    regions = ["us-east", "europe-west", "asia-southeast"]
    
    print(f"Starting data collection across {len(keywords)} keywords and {len(regions)} regions...")
    training_data = await collect_training_data(keywords, regions)
    print(f"Successfully collected {len(training_data)} training samples")
    
    # In a real application, you would process and store the collected data
    
if __name__ == "__main__":
    # Modern Python async execution
    asyncio.run(main())