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/memory.rs

@@ -298,6 +298,16 @@ impl MemoryManager {
         Ok((data.memories, data.conversations))
     }
 
+    // Getter: 単一メモリ取得
+    pub fn get_memory(&self, id: &str) -> Option<&Memory> {
+        self.memories.get(id)
+    }
+
+    // Getter: 全メモリ取得
+    pub fn get_all_memories(&self) -> Vec<&Memory> {
+        self.memories.values().collect()
+    }
+
     fn save_data(&self) -> Result<()> {
         #[derive(Serialize)]
         struct Data<'a> {