Email Processing MCP Server

# Email Processing ## Overview The email processing pipeline handles the retrieval, analysis, and storage of email data across multiple databases. It implements a robust workflow for managing email content and metadata. ## Core Components ### OutlookConnector ```python class OutlookConnector: # Manages Outlook connection # Retrieves email data from Inbox, Sent Items, and optionally Deleted Items # Handles authentication # Supports configurable folder processing ``` ### EmailProcessingThread ```python class EmailProcessingThread: # Orchestrates processing workflow # Manages database operations # Handles error recovery ``` ### EmailMetadata ```python class EmailMetadata: # Structured email data # Metadata extraction # Data validation ``` ## Processing Pipeline ### 1. Email Retrieval - Connect to Outlook - Fetch email content from configured folders - Process Inbox and Sent Items by default - Optionally process Deleted Items when enabled - Extract attachments - Clean HTML content ### 2. Content Processing - Text extraction - Image processing - URL extraction - Metadata compilation ### 3. Analysis - LLM categorization - Embedding generation - Summary creation - Relationship mapping ### 4. Storage - MongoDB metadata storage - ChromaDB embedding storage - Neo4j relationship creation ## Data Flow ### Input Processing 1. Raw email retrieval 2. Content cleaning 3. Metadata extraction 4. Attachment handling ### Analysis Pipeline 1. Text preprocessing 2. Category generation 3. Embedding creation 4. Summary generation ### Storage Operations 1. Metadata persistence 2. Vector storage 3. Graph creation 4. Status tracking ## Configuration ### Environment Variables ``` MONGODB_URI=mongodb://localhost:27017/MCP SQLITE_DB_PATH=C:\path\to\emails.db EMBEDDING_BASE_URL=http://localhost:11434 EMBEDDING_MODEL=nomic-embed-text COLLECTION_NAME=outlook-emails PROCESS_DELETED_ITEMS=true|false ``` ### Processing Options - Batch size - Concurrency - Retry settings - Timeout values ## Error Handling ### Recovery Mechanisms - Connection retries - Partial updates - State tracking - Cleanup operations ### Logging - Operation status - Error details - Performance metrics - Progress tracking ## Best Practices ### Performance - Batch processing - Asynchronous operations - Resource management - Connection pooling ### Data Integrity - Validation checks - Atomic operations - Consistency verification - Error recovery ### Monitoring - Progress tracking - Status reporting - Error notification - Performance metrics ## Integration Points ### Database Handlers - MongoDB operations - ChromaDB interactions - Neo4j transactions ### User Interface - Progress updates - Status reporting - Error notifications - Operation control