MCP Orchestration Server

#!/usr/bin/env python3 """ Document Processor Agent - Core agent for processing documents """ # import asyncio # import json import re import sys import os from typing import Dict, List, Any, Optional from datetime import datetime # Add project root to path sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) from agents.base_agent import BaseMCPAgent, AgentCapability, MCPMessage # MongoDB integration try: from mcp_mongodb_integration import MCPMongoDBIntegration MONGODB_AVAILABLE = True except ImportError: MONGODB_AVAILABLE = False # Agent metadata for auto-discovery AGENT_METADATA = { "id": "document_processor", "name": "Document Processor Agent", "version": "1.0.0", "author": "MCP System", "description": "Extract text, detect authors, process PDFs and images", "dependencies": [], "auto_load": True, "priority": 1 } class DocumentProcessorAgent(BaseMCPAgent): """MCP Agent for processing documents with inter-agent communication.""" def __init__(self): capabilities = [ AgentCapability( name="document_processing", description="Extract text, detect authors, process PDFs and images", input_types=["pdf", "image", "text"], output_types=["text", "dict"], methods=["process", "extract_text", "detect_authors", "extract_metadata"], can_call_agents=["text_analyzer"] ) ] super().__init__("document_processor", "Document Processor Agent", capabilities) # Initialize MongoDB integration self.mongodb_integration = None if MONGODB_AVAILABLE: try: import asyncio self.mongodb_integration = MCPMongoDBIntegration() asyncio.create_task(self._init_mongodb()) except Exception as e: self.logger.error(f"Failed to initialize MongoDB: {e}") async def _init_mongodb(self): """Initialize MongoDB connection.""" if self.mongodb_integration: try: connected = await self.mongodb_integration.connect() if connected: self.logger.info("Document Agent connected to MongoDB") else: self.logger.warning("Document Agent failed to connect to MongoDB") except Exception as e: self.logger.error(f"Document Agent MongoDB initialization error: {e}") async def _store_document_result(self, input_data: Dict[str, Any], result: Dict[str, Any]): """Store document processing result in MongoDB with force storage.""" if self.mongodb_integration: try: # Primary storage method mongodb_id = await self.mongodb_integration.save_agent_output( "document_agent", input_data, result, {"storage_type": "document_processing", "processing_type": "document_analysis"} ) self.logger.info(f"✅ Document result stored in MongoDB: {mongodb_id}") # Also force store as backup await self.mongodb_integration.force_store_result( "document_agent", input_data.get("user_input", "document_processing"), result ) self.logger.info("✅ Document result force stored as backup") except Exception as e: self.logger.error(f"❌ Failed to store document result: {e}") # Try force storage as fallback try: await self.mongodb_integration.force_store_result( "document_agent", input_data.get("user_input", "document_processing"), result ) self.logger.info("✅ Document result fallback storage successful") except Exception as e2: self.logger.error(f"❌ Document result fallback storage failed: {e2}") async def handle_process(self, message: MCPMessage) -> Dict[str, Any]: """Process documents and collaborate with other agents.""" params = message.params user_input = params.get("user_input", "") context = params.get("context", {}) documents = context.get("documents_context", []) if not documents: return { "status": "no_documents", "message": "No documents provided for processing", "agent": self.agent_id } # Process each document processed_docs = [] all_authors = [] all_content = [] for doc in documents: processed_doc = await self.process_single_document(doc) processed_docs.append(processed_doc) if processed_doc.get("authors"): all_authors.extend(processed_doc["authors"]) if processed_doc.get("content"): all_content.append(processed_doc["content"]) # If user is asking about authors, collaborate with text analyzer if "author" in user_input.lower(): try: text_analysis = await self.call_agent( "text_analyzer", "find_authors", {"content": " ".join(all_content), "detected_authors": all_authors} ) return { "status": "success", "processed_documents": processed_docs, "author_analysis": text_analysis, "collaboration": ["text_analyzer"], "agent": self.agent_id } except Exception as e: self.log_error(f"Author collaboration error: {e}") # Default processing result = { "status": "success", "processed_documents": processed_docs, "total_documents": len(processed_docs), "authors_found": list(set(all_authors)), "agent": self.agent_id } # Store in MongoDB await self._store_document_result(params, result) return result async def process_single_document(self, doc: Dict[str, Any]) -> Dict[str, Any]: """Process a single document.""" filename = doc.get("filename", "unknown") content = doc.get("content", "") doc_type = doc.get("type", "unknown") # Extract authors from content authors = self.extract_authors_from_content(content) # Extract metadata metadata = { "filename": filename, "type": doc_type, "size": len(content), "word_count": len(content.split()) if content else 0, "processed_at": datetime.now().isoformat() } return { "filename": filename, "content": content, "authors": authors, "metadata": metadata, "processed_by": self.agent_id } def extract_authors_from_content(self, content: str) -> List[str]: """Extract authors from document content.""" authors = [] # Look for explicit author mentions author_patterns = [ r"Authors?:\s*([^\n]+)", r"Detected Authors?:\s*([^\n]+)", r"By:\s*([^\n]+)", r"Written by:\s*([^\n]+)" ] for pattern in author_patterns: matches = re.findall(pattern, content, re.IGNORECASE) for match in matches: # Split by commas and clean up author_names = [name.strip() for name in match.split(",")] authors.extend(author_names) # Remove duplicates and empty strings authors = list(set([author for author in authors if author])) return authors async def handle_extract_text(self, message: MCPMessage) -> Dict[str, Any]: """Extract text from documents.""" params = message.params documents = params.get("documents", []) extracted_texts = [] for doc in documents: content = doc.get("content", "") extracted_texts.append({ "filename": doc.get("filename", "unknown"), "text": content, "word_count": len(content.split()) if content else 0 }) return { "status": "success", "extracted_texts": extracted_texts, "total_documents": len(documents), "agent": self.agent_id } async def handle_detect_authors(self, message: MCPMessage) -> Dict[str, Any]: """Detect authors in documents.""" params = message.params content = params.get("content", "") documents = params.get("documents", []) all_authors = [] # Process content directly if content: authors = self.extract_authors_from_content(content) all_authors.extend(authors) # Process documents for doc in documents: doc_content = doc.get("content", "") authors = self.extract_authors_from_content(doc_content) all_authors.extend(authors) # Remove duplicates unique_authors = list(set(all_authors)) return { "status": "success", "authors": unique_authors, "total_authors": len(unique_authors), "agent": self.agent_id } async def handle_extract_metadata(self, message: MCPMessage) -> Dict[str, Any]: """Extract metadata from documents.""" params = message.params documents = params.get("documents", []) metadata_list = [] for doc in documents: content = doc.get("content", "") metadata = { "filename": doc.get("filename", "unknown"), "type": doc.get("type", "unknown"), "size": len(content), "word_count": len(content.split()) if content else 0, "character_count": len(content), "line_count": len(content.split('\n')) if content else 0, "extracted_at": datetime.now().isoformat() } metadata_list.append(metadata) return { "status": "success", "metadata": metadata_list, "total_documents": len(documents), "agent": self.agent_id } # Agent registration functions def get_agent_info(): """Get agent information for auto-discovery.""" return { "name": "Document Processor Agent", "description": "Extract text, detect authors, process PDFs and images with inter-agent communication", "version": "1.0.0", "author": "MCP System", "capabilities": ["document_processing", "text_extraction", "author_detection", "metadata_extraction"], "category": "core" } def create_agent(): """Create and return the agent instance.""" return DocumentProcessorAgent() if __name__ == "__main__": # Test the Document Processor agent print("📄 Testing Document Processor Agent") print("=" * 40) agent = DocumentProcessorAgent() print(f"Created agent: {agent}") print(f"Capabilities: {[cap.name for cap in agent.capabilities]}") print(f"Methods: {list(agent.message_handlers.keys())}") print("\n✅ Document Processor agent ready!") print("🎯 Perfect for processing documents and collaborating with other agents!")