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

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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)
This commit is contained in:
Claude
2025-11-05 14:17:14 +00:00
parent fd97ba2d81
commit 00c26f5984
5 changed files with 728 additions and 2 deletions
Download Patch File Download Diff File Expand all files Collapse all files

108
src/mcp/base.rs
View File

@@ -156,6 +156,43 @@ impl BaseMCPServer {
"type": "object",
"properties": {}
}
}),
json!({
"name": "create_memory_with_ai",
"description": "Create a new memory with AI interpretation and priority scoring",
"inputSchema": {
"type": "object",
"properties": {
"content": {
"type": "string",
"description": "Content of the memory"
},
"user_context": {
"type": "string",
"description": "User-specific context (optional)"
}
},
"required": ["content"]
}
}),
json!({
"name": "list_memories_by_priority",
"description": "List memories sorted by priority score (high to low)",
"inputSchema": {
"type": "object",
"properties": {
"min_score": {
"type": "number",
"description": "Minimum priority score (0.0-1.0)",
"minimum": 0.0,
"maximum": 1.0
},
"limit": {
"type": "integer",
"description": "Maximum number of memories to return"
}
}
}
})
]
}
@@ -183,6 +220,8 @@ impl BaseMCPServer {
pub async fn execute_tool(&mut self, tool_name: &str, arguments: &Value) -> Value {
match tool_name {
"create_memory" => self.tool_create_memory(arguments),
"create_memory_with_ai" => self.tool_create_memory_with_ai(arguments).await,
"list_memories_by_priority" => self.tool_list_memories_by_priority(arguments),
"search_memories" => self.tool_search_memories(arguments),
"update_memory" => self.tool_update_memory(arguments),
"delete_memory" => self.tool_delete_memory(arguments),
@@ -218,6 +257,9 @@ impl BaseMCPServer {
"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<_>>()
@@ -266,6 +308,72 @@ impl BaseMCPServer {
})
}
// AI解釈付きメモリ作成
async fn tool_create_memory_with_ai(&mut self, arguments: &Value) -> Value {
let content = arguments["content"].as_str().unwrap_or("");
let user_context = arguments["user_context"].as_str();
match self.memory_manager.create_memory_with_ai(content, user_context).await {
Ok(id) => {
// 作成したメモリを取得して詳細情報を返す
if let Some(memory) = self.memory_manager.get_memory(&id) {
json!({
"success": true,
"id": id,
"memory": {
"content": memory.content,
"interpreted_content": memory.interpreted_content,
"priority_score": memory.priority_score,
"user_context": memory.user_context,
"created_at": memory.created_at
},
"message": "Memory created with AI interpretation and priority scoring"
})
} else {
json!({
"success": true,
"id": id,
"message": "Memory created with AI interpretation"
})
}
}
Err(e) => json!({
"success": false,
"error": e.to_string()
})
}
}
// 優先順位順にメモリをリスト
fn tool_list_memories_by_priority(&self, arguments: &Value) -> Value {
let min_score = arguments["min_score"].as_f64().unwrap_or(0.0) as f32;
let limit = arguments["limit"].as_u64().map(|l| l as usize);
let mut memories = self.memory_manager.get_memories_by_priority();
// min_scoreでフィルタリング
memories.retain(|m| m.priority_score >= min_score);
// limitを適用
if let Some(limit) = limit {
memories.truncate(limit);
}
json!({
"success": true,
"count": memories.len(),
"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<_>>()
})
}
// 不明なメソッドハンドラ
fn handle_unknown_method(&self, id: Value) -> Value {
json!({

3
src/mcp/extended.rs
View File

@@ -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<_>>(),

10
src/memory.rs
View File

@@ -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> {