Email Processing MCP Server
by Cam10001110101
Verified
- .context
- modules
# 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