Email Processing MCP Server

# Analysis Tools ## Overview The analysis tools provide advanced capabilities for email categorization, summarization, and relationship analysis using LLMs and other AI techniques. ## Upcoming Features These features are planned for future releases: - Email search with semantic capabilities - Email summarization using LLMs - Automatic email categorization - Customizable email reports - Advanced filtering options - Outlook drafting email responses - Outlook rule suggestions - Expanded database options with Neo4j and ChromaDB integration ## Categorizer ### Implementation (Categorizer.py) ```python class Categorizer: # LLM-based email categorization # Category management # Classification logic ``` ### Features - Content analysis - Category generation - Confidence scoring - Category management ## Email Summarization ### Implementation (SummarizeEmails.py) ```python class SummarizeEmails: # Email content summarization # Thread summarization # Key point extraction ``` ### Capabilities - Individual email summaries - Thread summaries - Key information extraction - Priority assessment ## Semantic Analysis ### Embedding Generation - Ollama embeddings via langchain_ollama - Vector representation stored in MongoDB - Similarity computation - Support for processing emails from all configured folders ### Search Capabilities - Semantic similarity - Content matching - Related email finding - Pattern detection ## Relationship Analysis ### Graph Analytics - Communication patterns - Network visualization - Centrality analysis - Community detection ### Temporal Analysis - Time-based patterns - Thread tracking - Response analysis - Activity monitoring ## Integration ### Database Integration - MongoDB for metadata and embeddings - SQLite for primary email storage - Proper connection management - Support for all email folders including Deleted Items when enabled ### UI Integration - Result visualization - Interactive analysis - Pattern exploration - Data filtering ## Best Practices ### Performance - Batch processing - Caching strategies - Resource management - Optimization techniques ### Accuracy - Model validation - Result verification - Quality metrics - Continuous improvement ### Scalability - Distributed processing - Resource allocation - Load management - Performance monitoring ## Configuration ### Model Settings - LLM parameters - Embedding configuration - Analysis thresholds - Performance tuning ### Processing Options - Batch sizes - Thread limits - Timeout settings - Resource constraints ## Error Handling ### Recovery - Model fallbacks - Error correction - State recovery - Result validation ### Monitoring - Performance tracking - Error logging - Quality metrics - Resource usage