Connect Factory Agents to Enterprise Data Sources via Tool Protocols with MCP SDK and LangGraph

"""
Production implementation for connecting factory agents to enterprise data sources via Tool Protocols with MCP SDK and LangGraph.
Provides secure, scalable operations.
"""

from typing import Dict, Any, List
import os
import logging
import httpx
import asyncio
from pydantic import BaseModel, ValidationError

# Logging setup
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to manage environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    api_url: str = os.getenv('API_URL')

# Helper Functions
async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'factory_id' not in data:
        raise ValueError('Missing factory_id')
    if not isinstance(data['factory_id'], str):
        raise ValueError('factory_id must be a string')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    return {key: str(value).strip() for key, value in data.items()}

async def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize input data for processing.
    
    Args:
        data: Input data to normalize
    Returns:
        Normalized data
    """
    return {k.lower(): v for k, v in data.items()}

async def fetch_data(endpoint: str) -> Dict[str, Any]:
    """Fetch data from an external API.
    
    Args:
        endpoint: API endpoint to fetch data from
    Returns:
        Response data
    Raises:
        Exception: If fetch fails
    """
    async with httpx.AsyncClient() as client:
        response = await client.get(endpoint)
        response.raise_for_status()  # Raise error for bad responses
        return response.json()

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save data to the database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If save fails
    """
    logger.info('Saving data to database...')
    # Simulate saving data to the database
    await asyncio.sleep(1)  # Simulate I/O delay
    logger.info('Data saved successfully.')

async def handle_errors(func):
    """Error handling decorator.
    
    Args:
        func: Function to decorate
    """
    async def wrapper(*args, **kwargs):
        try:
            return await func(*args, **kwargs)
        except ValidationError as ve:
            logger.error(f'Validation error: {ve}')
            raise
        except Exception as e:
            logger.error(f'Error occurred: {e}')
            raise
    return wrapper

class DataProcessor:
    """Main orchestrator for processing data.
    """

    def __init__(self, config: Config):
        self.config = config

    @handle_errors
    async def process(self, input_data: Dict[str, Any]) -> None:
        """Main processing workflow.
        
        Args:
            input_data: Data to process
        """
        await validate_input(input_data)  # Validate input
        sanitized_data = await sanitize_fields(input_data)  # Sanitize
        normalized_data = await normalize_data(sanitized_data)  # Normalize
        api_response = await fetch_data(self.config.api_url)  # Fetch from API
        await save_to_db(api_response)  # Save to DB

if __name__ == '__main__':
    # Example usage
    config = Config()
    processor = DataProcessor(config)
    example_data = {'factory_id': '12345'}
    asyncio.run(processor.process(example_data))