Refactor: Integrate AI features with MCP tools and add technical review

Critical improvements based on technical review:

## Fixed Issues (Priority: High)
1. AI features now properly integrated with MCP server
   - Added create_memory_with_ai tool (was implemented but unused!)
   - Added list_memories_by_priority tool
   - All memory outputs now include new fields: interpreted_content, priority_score, user_context

2. Added getter methods to MemoryManager
   - get_memory(id) for single memory retrieval
   - get_all_memories() for bulk access

3. Complete memory information in MCP responses
   - search_memories now returns all fields
   - Priority-based filtering and sorting functional

## New Files
- docs/TECHNICAL_REVIEW.md: Comprehensive technical evaluation
  - Scores: 65/100 overall, identified key improvements
  - Actionable recommendations for Phase 1-3
  - Architecture proposals and code examples

## Updated Documentation
- README.md: Added usage examples for new AI tools
- Clear distinction between basic and AI-powered tools

## Technical Debt Identified
- openai crate version needs update (see review doc)
- Config externalization needed
- Test suite missing
- LLM provider abstraction recommended

This brings the implementation in line with the "psychological priority memory"
concept. The AI interpretation and scoring features are now actually usable!

Next: Phase 2 improvements (config externalization, error handling)

src/mcp/extended.rs

@@ -210,6 +210,9 @@ impl ExtendedMCPServer {
             "memories": memories.into_iter().map(|m| json!({
                 "id": m.id,
                 "content": m.content,
+                "interpreted_content": m.interpreted_content,
+                "priority_score": m.priority_score,
+                "user_context": m.user_context,
                 "created_at": m.created_at,
                 "updated_at": m.updated_at
             })).collect::<Vec<_>>(),