refactor: 工具调用记录迁移至独立表并更新文档

backend/models.py

@@ -8,7 +8,7 @@ class User(db.Model):
 
     id = db.Column(db.BigInteger, primary_key=True, autoincrement=True)
     username = db.Column(db.String(50), unique=True, nullable=False)
-    password = db.Column(db.String(255), nullable=False)
+    password = db.Column(db.String(255), nullable=True)  # Allow NULL for third-party login
     phone = db.Column(db.String(20))
 
     conversations = db.relationship("Conversation", backref="user", lazy="dynamic",
@@ -20,7 +20,7 @@ class Conversation(db.Model):
     __tablename__ = "conversations"
 
     id = db.Column(db.String(64), primary_key=True)
-    user_id = db.Column(db.BigInteger, db.ForeignKey("users.id"), nullable=False)
+    user_id = db.Column(db.BigInteger, db.ForeignKey("users.id"), nullable=False, index=True)
     title = db.Column(db.String(255), nullable=False, default="")
     model = db.Column(db.String(64), nullable=False, default="glm-5")
     system_prompt = db.Column(db.Text, default="")
@@ -28,7 +28,8 @@ class Conversation(db.Model):
     max_tokens = db.Column(db.Integer, nullable=False, default=65536)
     thinking_enabled = db.Column(db.Boolean, default=False)
     created_at = db.Column(db.DateTime, default=lambda: datetime.now(timezone.utc))
-    updated_at = db.Column(db.DateTime, default=lambda: datetime.now(timezone.utc), onupdate=lambda: datetime.now(timezone.utc))
+    updated_at = db.Column(db.DateTime, default=lambda: datetime.now(timezone.utc), 
+                          onupdate=lambda: datetime.now(timezone.utc), index=True)
 
     messages = db.relationship("Message", backref="conversation", lazy="dynamic",
                                cascade="all, delete-orphan",
@@ -39,27 +40,48 @@ class Message(db.Model):
     __tablename__ = "messages"
 
     id = db.Column(db.String(64), primary_key=True)
-    conversation_id = db.Column(db.String(64), db.ForeignKey("conversations.id"), nullable=False)
+    conversation_id = db.Column(db.String(64), db.ForeignKey("conversations.id"), 
+                                nullable=False, index=True)
     role = db.Column(db.String(16), nullable=False)  # user, assistant, system, tool
-    content = db.Column(db.Text, default="")
+    content = db.Column(LONGTEXT, default="")  # LONGTEXT for long conversations
     token_count = db.Column(db.Integer, default=0)
-    thinking_content = db.Column(db.Text, default="")
+    thinking_content = db.Column(LONGTEXT, default="")
-    
+    created_at = db.Column(db.DateTime, default=lambda: datetime.now(timezone.utc), index=True)
-    # Tool call support
+
-    tool_calls = db.Column(LONGTEXT)  # JSON string: tool call requests (assistant messages)
+    # Tool call support - relation to ToolCall table
-    tool_call_id = db.Column(db.String(64))  # Tool call ID (tool messages)
+    tool_calls = db.relationship("ToolCall", backref="message", lazy="dynamic",
-    name = db.Column(db.String(64))  # Tool name (tool messages)
+                                 cascade="all, delete-orphan",
-    
+                                 order_by="ToolCall.call_index.asc()")
+
+
+class ToolCall(db.Model):
+    """Tool call record - separate table, follows database normalization"""
+    __tablename__ = "tool_calls"
+
+    id = db.Column(db.BigInteger, primary_key=True, autoincrement=True)
+    message_id = db.Column(db.String(64), db.ForeignKey("messages.id"), 
+                           nullable=False, index=True)
+    call_id = db.Column(db.String(64), nullable=False)  # Tool call ID
+    call_index = db.Column(db.Integer, nullable=False, default=0)  # Call order
+    tool_name = db.Column(db.String(64), nullable=False)  # Tool name
+    arguments = db.Column(LONGTEXT, nullable=False)  # Call arguments JSON
+    result = db.Column(LONGTEXT)  # Execution result JSON
+    execution_time = db.Column(db.Float, default=0)  # Execution time (seconds)
     created_at = db.Column(db.DateTime, default=lambda: datetime.now(timezone.utc))
 
+    __table_args__ = (
+        db.Index("ix_tool_calls_message_call", "message_id", "call_index"),
+    )
+
 
 class TokenUsage(db.Model):
     __tablename__ = "token_usage"
 
     id = db.Column(db.BigInteger, primary_key=True, autoincrement=True)
-    user_id = db.Column(db.BigInteger, db.ForeignKey("users.id"), nullable=False)
+    user_id = db.Column(db.BigInteger, db.ForeignKey("users.id"), 
-    date = db.Column(db.Date, nullable=False)  # 使用日期
+                        nullable=False, index=True)
-    model = db.Column(db.String(64), nullable=False)  # 模型名称
+    date = db.Column(db.Date, nullable=False, index=True)
+    model = db.Column(db.String(64), nullable=False)
     prompt_tokens = db.Column(db.Integer, default=0)
     completion_tokens = db.Column(db.Integer, default=0)
     total_tokens = db.Column(db.Integer, default=0)
@@ -67,4 +89,5 @@ class TokenUsage(db.Model):
 
     __table_args__ = (
         db.UniqueConstraint("user_id", "date", "model", name="uq_user_date_model"),
+        db.Index("ix_token_usage_date_model", "date", "model"),  # Composite index
     )

backend/services/chat.py

@@ -3,7 +3,7 @@ import json
 import uuid
 from flask import current_app, Response
 from backend import db
-from backend.models import Conversation, Message
+from backend.models import Conversation, Message, ToolCall
 from backend.tools import registry, ToolExecutor
 from backend.utils.helpers import (
     get_or_create_default_user,
@@ -60,8 +60,7 @@ class ChatService:
                 prompt_tokens = usage.get("prompt_tokens", 0)
                 completion_tokens = usage.get("completion_tokens", 0)
                 
-                merged_tool_calls = self._merge_tool_results(all_tool_calls, all_tool_results)
+                # Create message
-                
                 msg = Message(
                     id=str(uuid.uuid4()),
                     conversation_id=conv.id,
@@ -69,16 +68,18 @@ class ChatService:
                     content=message.get("content", ""),
                     token_count=completion_tokens,
                     thinking_content=message.get("reasoning_content", ""),
-                    tool_calls=json.dumps(merged_tool_calls) if merged_tool_calls else None
                 )
                 db.session.add(msg)
+                
+                # Create tool call records
+                self._save_tool_calls(msg.id, all_tool_calls, all_tool_results)
                 db.session.commit()
                 
                 user = get_or_create_default_user()
                 record_token_usage(user.id, conv.model, prompt_tokens, completion_tokens)
                 
                 return ok({
-                    "message": to_dict(msg, thinking_content=msg.thinking_content or None),
+                    "message": self._message_to_dict(msg),
                     "usage": {
                         "prompt_tokens": prompt_tokens,
                         "completion_tokens": completion_tokens,
@@ -194,8 +195,6 @@ class ChatService:
                     continue
                 
                 # No tool calls - finish
-                merged_tool_calls = self._merge_tool_results(all_tool_calls, all_tool_results)
-                
                 with app.app_context():
                     msg = Message(
                         id=msg_id,
@@ -204,9 +203,11 @@ class ChatService:
                         content=full_content,
                         token_count=token_count,
                         thinking_content=full_thinking,
-                        tool_calls=json.dumps(merged_tool_calls) if merged_tool_calls else None
                     )
                     db.session.add(msg)
+                    
+                    # Create tool call records
+                    self._save_tool_calls(msg_id, all_tool_calls, all_tool_results)
                     db.session.commit()
                     
                     user = get_or_create_default_user()
@@ -223,6 +224,53 @@ class ChatService:
             headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"}
         )
     
+    def _save_tool_calls(self, message_id: str, tool_calls: list, tool_results: list) -> None:
+        """Save tool calls to database"""
+        for i, tc in enumerate(tool_calls):
+            result_content = tool_results[i]["content"] if i < len(tool_results) else None
+            
+            # Parse result to extract execution_time if present
+            execution_time = 0
+            if result_content:
+                try:
+                    result_data = json.loads(result_content)
+                    execution_time = result_data.get("execution_time", 0)
+                except:
+                    pass
+            
+            tool_call = ToolCall(
+                message_id=message_id,
+                call_id=tc.get("id", ""),
+                call_index=i,
+                tool_name=tc["function"]["name"],
+                arguments=tc["function"]["arguments"],
+                result=result_content,
+                execution_time=execution_time,
+            )
+            db.session.add(tool_call)
+    
+    def _message_to_dict(self, msg: Message) -> dict:
+        """Convert message to dict with tool calls"""
+        result = to_dict(msg, thinking_content=msg.thinking_content or None)
+        
+        # Add tool calls if any
+        tool_calls = msg.tool_calls.all() if msg.tool_calls else []
+        if tool_calls:
+            result["tool_calls"] = [
+                {
+                    "id": tc.call_id,
+                    "type": "function",
+                    "function": {
+                        "name": tc.tool_name,
+                        "arguments": tc.arguments,
+                    },
+                    "result": tc.result,
+                }
+                for tc in tool_calls
+            ]
+        
+        return result
+    
     def _process_tool_calls_delta(self, delta: dict, tool_calls_list: list) -> list:
         """Process tool calls from streaming delta"""
         tool_calls_delta = delta.get("tool_calls", [])
@@ -242,13 +290,3 @@ class ChatService:
                 if tc["function"].get("arguments"):
                     tool_calls_list[idx]["function"]["arguments"] += tc["function"]["arguments"]
         return tool_calls_list
-    
-    def _merge_tool_results(self, tool_calls: list, tool_results: list) -> list:
-        """Merge tool results into tool calls"""
-        merged = []
-        for i, tc in enumerate(tool_calls):
-            merged_tc = dict(tc)
-            if i < len(tool_results):
-                merged_tc["result"] = tool_results[i]["content"]
-            merged.append(merged_tc)
-        return merged

backend/utils/helpers.py

@@ -9,7 +9,7 @@ def get_or_create_default_user():
     """Get or create default user"""
     user = User.query.filter_by(username="default").first()
     if not user:
-        user = User(username="default", password="")
+        user = User(username="default", password=None)
         db.session.add(user)
         db.session.commit()
     return user
@@ -39,13 +39,6 @@ def to_dict(inst, **extra):
         if k in d and hasattr(d[k], "strftime"):
             d[k] = d[k].strftime("%Y-%m-%dT%H:%M:%SZ")
     
-    # Parse tool_calls JSON if present
-    if "tool_calls" in d and d["tool_calls"]:
-        try:
-            d["tool_calls"] = json.loads(d["tool_calls"])
-        except:
-            pass
-    
     # Filter out None values for cleaner API response
     d = {k: v for k, v in d.items() if v is not None}
     

docs/Design.md

@@ -526,15 +526,24 @@ GET /api/stats/tokens?period=daily
 ### ER 关系
 
 ```
-User  1 ── * Conversation  1 ── * Message
+User  1 ── * Conversation  1 ── * Message  1 ── * ToolCall
 ```
 
+### User（用户）
+
+| 字段         | 类型            | 说明                    |
+| ------------ | --------------- | ----------------------- |
+| `id`         | bigint          | 用户 ID（自增）           |
+| `username`   | string(50)      | 用户名（唯一）            |
+| `password`   | string(255)     | 密码（可为空，第三方登录）  |
+| `phone`      | string(20)      | 手机号                  |
+
 ### Conversation（会话）
 
 | 字段                 | 类型            | 说明                    |
 | ------------------ | ------------- | --------------------- |
 | `id`               | string (UUID) | 会话 ID                 |
-| `user_id`          | string        | 所属用户 ID               |
+| `user_id`          | bigint        | 所属用户 ID               |
 | `title`            | string        | 会话标题                  |
 | `model`            | string        | 使用的模型，默认 `glm-5`      |
 | `system_prompt`    | string        | 系统提示词                 |
@@ -550,21 +559,51 @@ User  1 ── * Conversation  1 ── * Message
 | ------------------ | ------------- | ------------------------------- |
 | `id`               | string (UUID) | 消息 ID                           |
 | `conversation_id`  | string        | 所属会话 ID                         |
-| `role`             | enum          | `user` / `assistant` / `system`  |
+| `role`             | enum          | `user` / `assistant` / `system` / `tool` |
-| `content`          | string        | 消息内容                            |
+| `content`          | LONGTEXT      | 消息内容                            |
 | `token_count`      | integer       | token 消耗数                       |
-| `thinking_content` | string        | 思维链内容（启用时）                      |
+| `thinking_content` | LONGTEXT      | 思维链内容（启用时）                      |
-| `tool_calls`       | array (JSON)  | 工具调用信息（含结果），仅 assistant 消息      |
 | `created_at`       | datetime      | 创建时间                            |
 
+**说明**：工具调用信息存储在关联的 `ToolCall` 表中，通过 `message.tool_calls` 关系获取。
+
+### ToolCall（工具调用）
+
+| 字段              | 类型            | 说明                        |
+| ----------------- | --------------- | --------------------------- |
+| `id`              | bigint          | 调用记录 ID（自增）            |
+| `message_id`      | string(64)      | 关联的消息 ID                 |
+| `call_id`         | string(64)      | 工具调用 ID                   |
+| `call_index`      | integer         | 调用顺序（从 0 开始）           |
+| `tool_name`       | string(64)      | 工具名称                      |
+| `arguments`       | LONGTEXT        | 调用参数 JSON                 |
+| `result`          | LONGTEXT        | 执行结果 JSON                 |
+| `execution_time`  | float           | 执行时间（秒）                  |
+| `created_at`      | datetime        | 创建时间                      |
+
+### TokenUsage（Token 使用统计）
+
+| 字段                | 类型       | 说明                       |
+| ------------------- | ---------- | -------------------------- |
+| `id`                | bigint     | 记录 ID（自增）             |
+| `user_id`           | bigint     | 用户 ID                    |
+| `date`              | date       | 统计日期                   |
+| `model`             | string(64) | 模型名称                   |
+| `prompt_tokens`     | integer    | 输入 token 数              |
+| `completion_tokens` | integer    | 输出 token 数              |
+| `total_tokens`      | integer    | 总 token 数                |
+| `created_at`        | datetime   | 创建时间                   |
+
 #### 消息类型说明
 
 **1. 用户消息 (role=user)**
 ```json
 {
   "id": "msg_001",
+  "conversation_id": "conv_abc123",
   "role": "user",
   "content": "北京今天天气怎么样？",
+  "token_count": 0,
   "created_at": "2026-03-24T10:00:00Z"
 }
 ```
@@ -573,22 +612,23 @@ User  1 ── * Conversation  1 ── * Message
 ```json
 {
   "id": "msg_002",
+  "conversation_id": "conv_abc123",
   "role": "assistant",
   "content": "北京今天天气晴朗...",
   "token_count": 50,
   "thinking_content": "用户想了解天气...",
-  "tool_calls": null,
   "created_at": "2026-03-24T10:00:01Z"
 }
 ```
 
 **3. 助手消息 - 含工具调用 (role=assistant, with tool_calls)**
 
-工具调用结果直接合并到 `tool_calls` 数组中，每个调用包含 `result` 字段：
+工具调用记录存储在独立的 `tool_calls` 表中，API 响应时会自动关联并返回：
 
 ```json
 {
   "id": "msg_003",
+  "conversation_id": "conv_abc123",
   "role": "assistant",
   "content": "北京今天天气晴朗，温度25°C，湿度60%",
   "token_count": 80,
@@ -608,6 +648,18 @@ User  1 ── * Conversation  1 ── * Message
 }
 ```
 
+**4. 工具消息 (role=tool)**
+
+用于 API 调用时传递工具执行结果（不存储在数据库）：
+```json
+{
+  "role": "tool",
+  "tool_call_id": "call_abc123",
+  "name": "get_weather",
+  "content": "{\"temperature\": 25, \"humidity\": 60}"
+}
+```
+
 #### 工具调用流程示例
 
 ```
@@ -617,14 +669,16 @@ User  1 ── * Conversation  1 ── * Message
     ↓
 [AI 调用工具 get_weather]
     ↓
-[msg_002] role=assistant, tool_calls=[{get_weather, args:{"city":"北京"}, result="{...}"}]
+[msg_002] role=assistant, content="北京今天天气晴朗，温度25°C..."
-          content="北京今天天气晴朗，温度25°C..."
+          tool_calls=[{get_weather, args:{"city":"北京"}, result="{...}"}]
 ```
 
 **说明：**
-- 工具调用结果直接存储在 `tool_calls[].result` 字段中
+- 工具调用记录存储在独立的 `tool_calls` 表中，与 `messages` 表通过 `message_id` 关联
-- 不再创建独立的 `role=tool` 消息
+- API 响应时自动查询并组装 `tool_calls` 数组
-- 前端可通过 `tool_calls` 数组展示完整的工具调用过程
+- 工具调用包含完整的调用参数和执行结果
+- `call_index` 字段记录同一消息中多次工具调用的顺序
+- `execution_time` 字段记录工具执行耗时
 
 ---
 

docs/ToolSystemDesign.md

@@ -2,7 +2,7 @@
 
 ## 概述
 
-本文档描述 NanoClaw 工具调用系统的设计，采用简化的工厂模式，减少不必要的类层次。
+NanoClaw 工具调用系统采用简化的工厂模式，支持装饰器注册、缓存优化、重复调用检测等功能。
 
 ---
 
@@ -27,13 +27,20 @@ classDiagram
         +register(ToolDefinition tool) void
         +get(str name) ToolDefinition?
         +list_all() list~dict~
-        +execute(str name, dict args) Any
+        +list_by_category(str category) list~dict~
+        +execute(str name, dict args) dict
+        +remove(str name) bool
+        +has(str name) bool
     }
 
     class ToolExecutor {
         -ToolRegistry registry
-        +process_tool_calls(list tool_calls) list~dict~
+        -dict _cache
-        +build_request(list messages) dict
+        -list _call_history
+        +process_tool_calls(list tool_calls, dict context) list~dict~
+        +build_request(list messages, str model, list tools, dict kwargs) dict
+        +clear_history() void
+        +execute_with_retry(str name, dict args, int max_retries) dict
     }
 
     class ToolResult {
@@ -42,6 +49,8 @@ classDiagram
         +Any data
         +str? error
         +dict to_dict()
+        +ok(Any data)$ ToolResult
+        +fail(str error)$ ToolResult
     }
 
     ToolRegistry "1" --> "*" ToolDefinition : manages
@@ -61,11 +70,8 @@ classDiagram
 
     class ToolFactory {
         <<module>>
-        +tool(name, description, parameters)$ decorator
+        +tool(name, description, parameters, category)$ decorator
-        +register(name, handler, description, parameters)$ void
+        +register_tool(name, handler, description, parameters, category)$ void
-        +create_crawler_tools()$ list~ToolDefinition~
-        +create_data_tools()$ list~ToolDefinition~
-        +create_file_tools()$ list~ToolDefinition~
     }
 
     class ToolDefinition {
@@ -73,6 +79,7 @@ classDiagram
         +str description
         +dict parameters
         +Callable handler
+        +str category
     }
 
     ToolFactory ..> ToolDefinition : creates
@@ -153,18 +160,31 @@ class ToolRegistry:
         return cls._instance
 
     def register(self, tool: ToolDefinition) -> None:
+        """注册工具"""
         self._tools[tool.name] = tool
 
     def get(self, name: str) -> Optional[ToolDefinition]:
+        """获取工具定义"""
         return self._tools.get(name)
 
     def list_all(self) -> List[dict]:
+        """列出所有工具（OpenAI 格式）"""
         return [t.to_openai_format() for t in self._tools.values()]
 
+    def list_by_category(self, category: str) -> List[dict]:
+        """按类别列出工具"""
+        return [
+            t.to_openai_format()
+            for t in self._tools.values()
+            if t.category == category
+        ]
+
     def execute(self, name: str, arguments: dict) -> dict:
+        """执行工具"""
         tool = self.get(name)
         if not tool:
             return ToolResult.fail(f"Tool not found: {name}").to_dict()
+
         try:
             result = tool.handler(arguments)
             if isinstance(result, ToolResult):
@@ -173,6 +193,17 @@ class ToolRegistry:
         except Exception as e:
             return ToolResult.fail(str(e)).to_dict()
 
+    def remove(self, name: str) -> bool:
+        """移除工具"""
+        if name in self._tools:
+            del self._tools[name]
+            return True
+        return False
+
+    def has(self, name: str) -> bool:
+        """检查工具是否存在"""
+        return name in self._tools
+
 
 # 全局注册表
 registry = ToolRegistry()
@@ -182,39 +213,205 @@ registry = ToolRegistry()
 
 ```python
 import json
-from typing import List, Dict
+import time
+import hashlib
+from typing import List, Dict, Optional
 
 class ToolExecutor:
-    """工具执行器"""
+    """工具执行器（支持缓存和重复检测）"""
 
-    def __init__(self, registry: ToolRegistry = None):
+    def __init__(
+        self,
+        registry: ToolRegistry = None,
+        api_url: str = None,
+        api_key: str = None,
+        enable_cache: bool = True,
+        cache_ttl: int = 300,  # 5分钟
+    ):
         self.registry = registry or ToolRegistry()
+        self.api_url = api_url
+        self.api_key = api_key
+        self.enable_cache = enable_cache
+        self.cache_ttl = cache_ttl
+        self._cache: Dict[str, tuple] = {}  # key -> (result, timestamp)
+        self._call_history: List[dict] = []  # 当前会话的调用历史
 
-    def process_tool_calls(self, tool_calls: List[dict]) -> List[dict]:
+    def _make_cache_key(self, name: str, args: dict) -> str:
-        """处理工具调用，返回消息列表"""
+        """生成缓存键"""
+        args_str = json.dumps(args, sort_keys=True, ensure_ascii=False)
+        return hashlib.md5(f"{name}:{args_str}".encode()).hexdigest()
+
+    def _get_cached(self, key: str) -> Optional[dict]:
+        """获取缓存结果"""
+        if not self.enable_cache:
+            return None
+        if key in self._cache:
+            result, timestamp = self._cache[key]
+            if time.time() - timestamp < self.cache_ttl:
+                return result
+            del self._cache[key]
+        return None
+
+    def _set_cache(self, key: str, result: dict) -> None:
+        """设置缓存"""
+        if self.enable_cache:
+            self._cache[key] = (result, time.time())
+
+    def _check_duplicate_in_history(self, name: str, args: dict) -> Optional[dict]:
+        """检查历史中是否有相同调用"""
+        args_str = json.dumps(args, sort_keys=True, ensure_ascii=False)
+        for record in self._call_history:
+            if record["name"] == name and record["args_str"] == args_str:
+                return record["result"]
+        return None
+
+    def clear_history(self) -> None:
+        """清空调用历史（新会话开始时调用）"""
+        self._call_history.clear()
+
+    def process_tool_calls(
+        self,
+        tool_calls: List[dict],
+        context: dict = None
+    ) -> List[dict]:
+        """
+        处理工具调用，返回消息列表
+
+        Args:
+            tool_calls: LLM 返回的工具调用列表
+            context: 可选上下文信息（user_id 等）
+
+        Returns:
+            工具响应消息列表，可直接追加到 messages
+        """
         results = []
+        seen_calls = set()  # 当前批次内的重复检测
+
         for call in tool_calls:
             name = call["function"]["name"]
-            args = json.loads(call["function"]["arguments"])
+            args_str = call["function"]["arguments"]
             call_id = call["id"]
 
+            try:
+                args = json.loads(args_str) if isinstance(args_str, str) else args_str
+            except json.JSONDecodeError:
+                results.append(self._create_error_result(
+                    call_id, name, "Invalid JSON arguments"
+                ))
+                continue
+
+            # 检查批次内重复
+            call_key = f"{name}:{json.dumps(args, sort_keys=True)}"
+            if call_key in seen_calls:
+                results.append(self._create_tool_result(
+                    call_id, name,
+                    {"success": True, "data": None, "cached": True, "duplicate": True}
+                ))
+                continue
+            seen_calls.add(call_key)
+
+            # 检查历史重复
+            history_result = self._check_duplicate_in_history(name, args)
+            if history_result is not None:
+                result = {**history_result, "cached": True}
+                results.append(self._create_tool_result(call_id, name, result))
+                continue
+
+            # 检查缓存
+            cache_key = self._make_cache_key(name, args)
+            cached_result = self._get_cached(cache_key)
+            if cached_result is not None:
+                result = {**cached_result, "cached": True}
+                results.append(self._create_tool_result(call_id, name, result))
+                continue
+
+            # 执行工具
             result = self.registry.execute(name, args)
 
-            results.append({
+            # 缓存结果
-                "role": "tool",
+            self._set_cache(cache_key, result)
-                "tool_call_id": call_id,
+
+            # 添加到历史
+            self._call_history.append({
                 "name": name,
-                "content": json.dumps(result, ensure_ascii=False)
+                "args_str": json.dumps(args, sort_keys=True, ensure_ascii=False),
+                "result": result
             })
+
+            results.append(self._create_tool_result(call_id, name, result))
+
         return results
 
-    def build_request(self, messages: List[dict], **kwargs) -> dict:
+    def _create_tool_result(
-        """构建 API 请求"""
+        self,
+        call_id: str,
+        name: str,
+        result: dict,
+        execution_time: float = 0
+    ) -> dict:
+        """创建工具结果消息"""
+        result["execution_time"] = execution_time
         return {
-            "model": kwargs.get("model", "glm-5"),
+            "role": "tool",
+            "tool_call_id": call_id,
+            "name": name,
+            "content": json.dumps(result, ensure_ascii=False, default=str)
+        }
+
+    def _create_error_result(
+        self,
+        call_id: str,
+        name: str,
+        error: str
+    ) -> dict:
+        """创建错误结果消息"""
+        return {
+            "role": "tool",
+            "tool_call_id": call_id,
+            "name": name,
+            "content": json.dumps({
+                "success": False,
+                "error": error
+            }, ensure_ascii=False)
+        }
+
+    def build_request(
+        self,
+        messages: List[dict],
+        model: str = "glm-5",
+        tools: List[dict] = None,
+        **kwargs
+    ) -> dict:
+        """构建 API 请求体"""
+        return {
+            "model": model,
             "messages": messages,
-            "tools": self.registry.list_all(),
+            "tools": tools or self.registry.list_all(),
-            "tool_choice": "auto"
+            "tool_choice": kwargs.get("tool_choice", "auto"),
+            **{k: v for k, v in kwargs.items() if k not in ["tool_choice"]}
+        }
+
+    def execute_with_retry(
+        self,
+        name: str,
+        arguments: dict,
+        max_retries: int = 3,
+        retry_delay: float = 1.0
+    ) -> dict:
+        """带重试的工具执行"""
+        last_error = None
+
+        for attempt in range(max_retries):
+            try:
+                return self.registry.execute(name, arguments)
+            except Exception as e:
+                last_error = e
+                if attempt < max_retries - 1:
+                    time.sleep(retry_delay)
+
+        return {
+            "success": False,
+            "error": f"Failed after {max_retries} retries: {last_error}"
         }
 ```
 
@@ -227,11 +424,30 @@ class ToolExecutor:
 ```python
 # backend/tools/factory.py
 
-from .core import ToolDefinition, registry
+from typing import Callable
+from backend.tools.core import ToolDefinition, registry
 
-def tool(name: str, description: str, parameters: dict, category: str = "general"):
+
-    """工具注册装饰器"""
+def tool(
-    def decorator(func):
+    name: str,
+    description: str,
+    parameters: dict,
+    category: str = "general"
+) -> Callable:
+    """
+    工具注册装饰器
+
+    用法:
+        @tool(
+            name="web_search",
+            description="搜索互联网获取信息",
+            parameters={"type": "object", "properties": {...}},
+            category="crawler"
+        )
+        def web_search(arguments: dict) -> dict:
+            ...
+    """
+    def decorator(func: Callable) -> Callable:
         tool_def = ToolDefinition(
             name=name,
             description=description,
@@ -242,6 +458,34 @@ def tool(name: str, description: str, parameters: dict, category: str = "general
         registry.register(tool_def)
         return func
     return decorator
+
+
+def register_tool(
+    name: str,
+    handler: Callable,
+    description: str,
+    parameters: dict,
+    category: str = "general"
+) -> None:
+    """
+    直接注册工具（无需装饰器）
+
+    用法:
+        register_tool(
+            name="my_tool",
+            handler=my_function,
+            description="工具描述",
+            parameters={...}
+        )
+    """
+    tool_def = ToolDefinition(
+        name=name,
+        description=description,
+        parameters=parameters,
+        handler=handler,
+        category=category
+    )
+    registry.register(tool_def)
 ```
 
 ### 4.2 使用示例
@@ -249,24 +493,32 @@ def tool(name: str, description: str, parameters: dict, category: str = "general
 ```python
 # backend/tools/builtin/crawler.py
 
-from ..factory import tool
+from backend.tools.factory import tool
+from backend.tools.services import SearchService, FetchService
 
 # 网页搜索工具
 @tool(
     name="web_search",
-    description="搜索互联网获取信息",
+    description="Search the internet for information. Use when you need to find latest news or answer questions that require web search.",
     parameters={
         "type": "object",
         "properties": {
-            "query": {"type": "string", "description": "搜索关键词"},
+            "query": {
-            "max_results": {"type": "integer", "default": 5}
+                "type": "string",
+                "description": "Search keywords"
+            },
+            "max_results": {
+                "type": "integer",
+                "description": "Number of results to return, default 5",
+                "default": 5
+            }
         },
         "required": ["query"]
     },
     category="crawler"
 )
 def web_search(arguments: dict) -> dict:
-    from ..services import SearchService
+    """Web search tool"""
     query = arguments["query"]
     max_results = arguments.get("max_results", 5)
     service = SearchService()
@@ -277,19 +529,27 @@ def web_search(arguments: dict) -> dict:
 # 页面抓取工具
 @tool(
     name="fetch_page",
-    description="抓取指定网页内容",
+    description="Fetch content from a specific webpage. Use when user needs detailed information from a webpage.",
     parameters={
         "type": "object",
         "properties": {
-            "url": {"type": "string", "description": "网页URL"},
+            "url": {
-            "extract_type": {"type": "string", "enum": ["text", "links", "structured"]}
+                "type": "string",
+                "description": "URL of the webpage to fetch"
+            },
+            "extract_type": {
+                "type": "string",
+                "description": "Extraction type",
+                "enum": ["text", "links", "structured"],
+                "default": "text"
+            }
         },
         "required": ["url"]
     },
     category="crawler"
 )
 def fetch_page(arguments: dict) -> dict:
-    from ..services import FetchService
+    """Page fetch tool"""
     url = arguments["url"]
     extract_type = arguments.get("extract_type", "text")
     service = FetchService()
@@ -297,33 +557,39 @@ def fetch_page(arguments: dict) -> dict:
     return result
 
 
-# 计算器工具
+# 批量抓取工具
 @tool(
-    name="calculator",
+    name="crawl_batch",
-    description="执行数学计算",
+    description="Batch fetch multiple webpages. Use when you need to get content from multiple pages at once.",
     parameters={
         "type": "object",
         "properties": {
-            "expression": {"type": "string", "description": "数学表达式"}
+            "urls": {
+                "type": "array",
+                "items": {"type": "string"},
+                "description": "List of URLs to fetch"
+            },
+            "extract_type": {
+                "type": "string",
+                "enum": ["text", "links", "structured"],
+                "default": "text"
+            }
         },
-        "required": ["expression"]
+        "required": ["urls"]
     },
-    category="data"
+    category="crawler"
 )
-def calculator(arguments: dict) -> dict:
+def crawl_batch(arguments: dict) -> dict:
-    import ast
+    """Batch fetch tool"""
-    import operator
+    urls = arguments["urls"]
-    expr = arguments["expression"]
+    extract_type = arguments.get("extract_type", "text")
-    # 安全计算
+
-    ops = {
+    if len(urls) > 10:
-        ast.Add: operator.add,
+        return {"error": "Maximum 10 pages can be fetched at once"}
-        ast.Sub: operator.sub,
+
-        ast.Mult: operator.mul,
+    service = FetchService()
-        ast.Div: operator.truediv
+    results = service.fetch_batch(urls, extract_type)
-    }
+    return {"results": results, "total": len(results)}
-    node = ast.parse(expr, mode='eval')
-    result = eval(compile(node, '<string>', 'eval'), {"__builtins__": {}}, ops)
-    return {"result": result}
 ```
 
 ---
@@ -332,90 +598,220 @@ def calculator(arguments: dict) -> dict:
 
 工具依赖的服务保持独立，不与工具类耦合：
 
-```mermaid
-classDiagram
-    direction LR
-
-    class SearchService {
-        -SearchEngine engine
-        +search(str query, int limit) list~dict~
-    }
-
-    class FetchService {
-        +fetch(str url, str type) dict
-        +fetch_batch(list urls) dict
-    }
-
-    class ContentExtractor {
-        +extract_text(html) str
-        +extract_links(html) list
-        +extract_structured(html) dict
-    }
-
-    FetchService --> ContentExtractor : uses
-```
-
 ```python
 # backend/tools/services.py
 
+from typing import List, Dict
+from ddgs import DDGS
+import re
+
+
 class SearchService:
     """搜索服务"""
-    def __init__(self, engine=None):
-        from ddgs import DDGS
-        self.engine = engine or DDGS()
 
-    def search(self, query: str, max_results: int = 5) -> list:
+    def __init__(self, engine: str = "duckduckgo"):
-        results = list(self.engine.text(query, max_results=max_results))
+        self.engine = engine
+
+    def search(
+        self,
+        query: str,
+        max_results: int = 5,
+        region: str = "cn-zh"
+    ) -> List[dict]:
+        """执行搜索"""
+        if self.engine == "duckduckgo":
+            return self._search_duckduckgo(query, max_results, region)
+        else:
+            raise ValueError(f"Unsupported search engine: {self.engine}")
+
+    def _search_duckduckgo(
+        self,
+        query: str,
+        max_results: int,
+        region: str
+    ) -> List[dict]:
+        """DuckDuckGo 搜索"""
+        with DDGS() as ddgs:
+            results = list(ddgs.text(
+                query,
+                max_results=max_results,
+                region=region
+            ))
+
         return [
-            {"title": r["title"], "url": r["href"], "snippet": r["body"]}
+            {
+                "title": r.get("title", ""),
+                "url": r.get("href", ""),
+                "snippet": r.get("body", "")
+            }
             for r in results
         ]
 
 
 class FetchService:
     """页面抓取服务"""
-    def __init__(self, timeout: float = 30.0):
+
+    def __init__(self, timeout: float = 30.0, user_agent: str = None):
         self.timeout = timeout
+        self.user_agent = user_agent or (
+            "Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
+            "AppleWebKit/537.36 (KHTML, like Gecko) "
+            "Chrome/120.0.0.0 Safari/537.36"
+        )
 
     def fetch(self, url: str, extract_type: str = "text") -> dict:
+        """抓取单个页面"""
         import httpx
-        from bs4 import BeautifulSoup
 
-        resp = httpx.get(url, timeout=self.timeout, follow_redirects=True)
+        try:
-        soup = BeautifulSoup(resp.text, "html.parser")
+            resp = httpx.get(
+                url,
+                timeout=self.timeout,
+                follow_redirects=True,
+                headers={"User-Agent": self.user_agent}
+            )
+            resp.raise_for_status()
+        except Exception as e:
+            return {"error": str(e), "url": url}
+
+        html = resp.text
+        extractor = ContentExtractor(html)
 
-        extractor = ContentExtractor(soup)
         if extract_type == "text":
-            return {"text": extractor.extract_text()}
+            return {
+                "url": url,
+                "text": extractor.extract_text()
+            }
         elif extract_type == "links":
-            return {"links": extractor.extract_links()}
+            return {
+                "url": url,
+                "links": extractor.extract_links()
+            }
         else:
-            return extractor.extract_structured()
+            return extractor.extract_structured(url)
+
+    def fetch_batch(
+        self,
+        urls: List[str],
+        extract_type: str = "text",
+        max_concurrent: int = 5
+    ) -> List[dict]:
+        """批量抓取页面"""
+        results = []
+        for url in urls:
+            results.append(self.fetch(url, extract_type))
+        return results
 
 
 class ContentExtractor:
     """内容提取器"""
-    def __init__(self, soup):
+
-        self.soup = soup
+    def __init__(self, html: str):
+        self.html = html
+        self._soup = None
+
+    @property
+    def soup(self):
+        if self._soup is None:
+            try:
+                from bs4 import BeautifulSoup
+                self._soup = BeautifulSoup(self.html, "html.parser")
+            except ImportError:
+                raise ImportError("Please install beautifulsoup4: pip install beautifulsoup4")
+        return self._soup
 
     def extract_text(self) -> str:
+        """提取纯文本"""
         # 移除脚本和样式
-        for tag in self.soup(["script", "style"]):
+        for tag in self.soup(["script", "style", "nav", "footer", "header"]):
             tag.decompose()
-        return self.soup.get_text(separator="\n", strip=True)
 
-    def extract_links(self) -> list:
+        text = self.soup.get_text(separator="\n", strip=True)
-        return [
+        # 清理多余空白
-            {"text": a.get_text(strip=True), "href": a.get("href")}
+        text = re.sub(r"\n{3,}", "\n\n", text)
-            for a in self.soup.find_all("a", href=True)
+        return text
-        ]
+
+    def extract_links(self) -> List[dict]:
+        """提取链接"""
+        links = []
+        for a in self.soup.find_all("a", href=True):
+            text = a.get_text(strip=True)
+            href = a["href"]
+            if text and href and not href.startswith(("#", "javascript:")):
+                links.append({"text": text, "href": href})
+        return links[:50]  # 限制数量
+
+    def extract_structured(self, url: str = "") -> dict:
+        """提取结构化内容"""
+        soup = self.soup
+
+        # 提取标题
+        title = ""
+        if soup.title:
+            title = soup.title.string or ""
+
+        # 提取 meta 描述
+        description = ""
+        meta_desc = soup.find("meta", attrs={"name": "description"})
+        if meta_desc:
+            description = meta_desc.get("content", "")
 
-    def extract_structured(self) -> dict:
         return {
-            "title": self.soup.title.string if self.soup.title else "",
+            "url": url,
-            "text": self.extract_text(),
+            "title": title.strip(),
+            "description": description.strip(),
+            "text": self.extract_text()[:5000],  # 限制长度
             "links": self.extract_links()[:20]
         }
+
+
+class CalculatorService:
+    """安全计算服务"""
+
+    ALLOWED_OPS = {
+        "add", "sub", "mul", "truediv", "floordiv",
+        "mod", "pow", "neg", "abs"
+    }
+
+    def evaluate(self, expression: str) -> dict:
+        """安全计算数学表达式"""
+        import ast
+        import operator
+
+        ops = {
+            ast.Add: operator.add,
+            ast.Sub: operator.sub,
+            ast.Mult: operator.mul,
+            ast.Div: operator.truediv,
+            ast.FloorDiv: operator.floordiv,
+            ast.Mod: operator.mod,
+            ast.Pow: operator.pow,
+            ast.USub: operator.neg,
+            ast.UAdd: operator.pos,
+        }
+
+        try:
+            # 解析表达式
+            node = ast.parse(expression, mode="eval")
+
+            # 验证节点类型
+            for child in ast.walk(node):
+                if isinstance(child, ast.Call):
+                    return {"error": "Function calls not allowed"}
+                if isinstance(child, ast.Name):
+                    return {"error": "Variable names not allowed"}
+
+            # 安全执行
+            result = eval(
+                compile(node, "<string>", "eval"),
+                {"__builtins__": {}},
+                {}
+            )
+
+            return {"result": result}
+
+        except Exception as e:
+            return {"error": f"Calculation error: {str(e)}"}
 ```
 
 ---
@@ -425,57 +821,154 @@ class ContentExtractor:
 ```python
 # backend/tools/__init__.py
 
-from .core import ToolDefinition, ToolResult, ToolRegistry, registry, ToolExecutor
+"""
-from .factory import tool
+NanoClaw Tool System
 
-def init_tools():
+Usage:
-    """初始化所有内置工具"""
+    from backend.tools import registry, ToolExecutor, tool
-    # 导入即自动注册
+    from backend.tools import init_tools
-    from .builtin import crawler, data, weather
 
-# 使用时
+    # 初始化内置工具
-init_tools()
+    init_tools()
+
+    # 列出所有工具
+    tools = registry.list_all()
+
+    # 执行工具
+    result = registry.execute("web_search", {"query": "Python"})
+"""
+
+from backend.tools.core import ToolDefinition, ToolResult, ToolRegistry, registry
+from backend.tools.factory import tool, register_tool
+from backend.tools.executor import ToolExecutor
+
+
+def init_tools() -> None:
+    """
+    初始化所有内置工具
+
+    导入 builtin 模块会自动注册所有装饰器定义的工具
+    """
+    from backend.tools.builtin import crawler, data, weather, file_ops  # noqa: F401
+
+
+# 公开 API 导出
+__all__ = [
+    # 核心类
+    "ToolDefinition",
+    "ToolResult",
+    "ToolRegistry",
+    "ToolExecutor",
+    # 实例
+    "registry",
+    # 工厂函数
+    "tool",
+    "register_tool",
+    # 初始化
+    "init_tools",
+]
 ```
 
 ---
 
 ## 七、工具清单
 
-| 类别      | 工具名称            | 描述   | 依赖服务          |
+### 7.1 爬虫工具 (crawler)
-| ------- | --------------- | ---- | ------------- |
+
-| crawler | `web_search`    | 网页搜索 | SearchService |
+| 工具名称          | 描述                          | 参数                                      |
-| crawler | `fetch_page`    | 单页抓取 | FetchService  |
+| --------------- | --------------------------- | --------------------------------------- |
-| crawler | `crawl_batch`   | 批量爬取 | FetchService  |
+| `web_search`    | 搜索互联网获取信息                   | `query`: 搜索关键词<br>`max_results`: 结果数量（默认 5） |
-| data    | `calculator`    | 数学计算 | CalculatorService |
+| `fetch_page`    | 抓取单个网页内容                    | `url`: 网页 URL<br>`extract_type`: 提取类型（text/links/structured） |
-| data    | `text_process`  | 文本处理 | -             |
+| `crawl_batch`   | 批量抓取多个网页（最多 10 个）           | `urls`: URL 列表<br>`extract_type`: 提取类型 |
-| data    | `json_process`  | JSON处理 | -             |
+
-| weather | `get_weather`   | 天气查询 | - (模拟数据)      |
+### 7.2 数据处理工具 (data)
-| file    | `file_read`     | 读取文件 | -             |
+
-| file    | `file_write`    | 写入文件 | -             |
+| 工具名称          | 描述                          | 参数                                      |
-| file    | `file_delete`   | 删除文件 | -             |
+| --------------- | --------------------------- | --------------------------------------- |
-| file    | `file_list`     | 列出目录 | -             |
+| `calculator`    | 执行数学计算（支持加减乘除、幂、模等）         | `expression`: 数学表达式                     |
-| file    | `file_exists`   | 检查存在 | -             |
+| `text_process`  | 文本处理（计数、格式转换等）              | `text`: 文本内容<br>`operation`: 操作类型（count/lines/words/upper/lower/reverse） |
-| file    | `file_mkdir`    | 创建目录 | -             |
+| `json_process`  | JSON 处理（解析、格式化、提取、验证）       | `json_string`: JSON 字符串<br>`operation`: 操作类型（parse/format/keys/validate） |
+
+### 7.3 天气工具 (weather)
+
+| 工具名称          | 描述                          | 参数                                      |
+| --------------- | --------------------------- | --------------------------------------- |
+| `get_weather`   | 查询指定城市的天气信息（模拟数据）           | `city`: 城市名称（如：北京、上海、广州）                |
+
+### 7.4 文件操作工具 (file)
+
+| 工具名称          | 描述                          | 参数                                      |
+| --------------- | --------------------------- | --------------------------------------- |
+| `file_read`     | 读取文件内容                      | `path`: 文件路径<br>`encoding`: 编码（默认 utf-8） |
+| `file_write`    | 写入文件（支持覆盖和追加）               | `path`: 文件路径<br>`content`: 内容<br>`mode`: 写入模式（write/append） |
+| `file_delete`   | 删除文件                        | `path`: 文件路径                            |
+| `file_list`     | 列出目录内容                      | `path`: 目录路径（默认 .）<br>`pattern`: 文件模式（默认 *） |
+| `file_exists`   | 检查文件或目录是否存在                 | `path`: 路径                              |
+| `file_mkdir`    | 创建目录（自动创建父目录）               | `path`: 目录路径                            |
+
+**安全说明**：文件操作工具限制在项目根目录内，防止越权访问。
 
 ---
 
 ## 八、与旧设计对比
 
-| 方面    | 旧设计               | 新设计       |
+| 方面      | 旧设计               | 新设计              |
-| ----- | ----------------- | --------- |
+| --------- | ----------------- | ----------------- |
-| 类数量   | 30+               | ~10       |
+| 类数量    | 30+               | ~10               |
-| 工具定义  | 继承 BaseTool       | 装饰器 + 函数  |
+| 工具定义  | 继承 BaseTool       | 装饰器 + 函数         |
-| 中间抽象层 | 5个（CrawlerTool 等） | 无         |
+| 中间抽象层 | 5个（CrawlerTool 等） | 无                 |
-| 扩展方式  | 创建子类              | 写函数 + 装饰器 |
+| 扩展方式  | 创建子类              | 写函数 + 装饰器         |
-| 代码量   | 多                 | 少         |
+| 缓存机制  | 无                 | 支持结果缓存（TTL 可配置）   |
+| 重复检测  | 无                 | 支持会话内重复调用检测       |
+| 代码量    | 多                 | 少                 |
 
 ---
 
-## 九、总结
+## 九、核心特性
 
-简化后的设计：
+### 9.1 装饰器注册
+
+简化工具定义，只需一个装饰器：
+
+```python
+@tool(
+    name="my_tool",
+    description="工具描述",
+    parameters={...},
+    category="custom"
+)
+def my_tool(arguments: dict) -> dict:
+    # 工具实现
+    return {"result": "ok"}
+```
+
+### 9.2 智能缓存
+
+- **结果缓存**：相同参数的工具调用结果会被缓存（默认 5 分钟）
+- **可配置 TTL**：通过 `cache_ttl` 参数设置缓存过期时间
+- **可禁用**：通过 `enable_cache=False` 关闭缓存
+
+### 9.3 重复检测
+
+- **批次内去重**：同一批次中相同工具+参数的调用会被跳过
+- **历史去重**：同一会话内已调用过的工具会直接返回缓存结果
+- **自动清理**：新会话开始时调用 `clear_history()` 清理历史
+
+### 9.4 安全设计
+
+- **计算器安全**：禁止函数调用和变量名，只支持数学运算
+- **文件沙箱**：文件操作限制在项目根目录内，防止越权访问
+- **错误处理**：所有工具执行都有 try-catch，不会因工具错误导致系统崩溃
+
+---
+
+## 十、总结
+
+简化后的设计特点：
 
 1. **核心类**：`ToolDefinition`、`ToolRegistry`、`ToolExecutor`、`ToolResult`
 2. **工厂模式**：使用 `@tool` 装饰器注册工具
 3. **服务分离**：工具依赖的服务独立，不与工具类耦合
-4. **易于扩展**：新增工具只需写一个函数并加装饰器
+4. **性能优化**：支持缓存和重复检测，减少重复计算和网络请求
+5. **易于扩展**：新增工具只需写一个函数并加装饰器
+6. **安全可靠**：文件沙箱、安全计算、完善的错误处理