remove：emoji插件，修复tool被截断的问题

src/maisaka/chat_loop_service.py

@@ -28,7 +28,7 @@ from src.llm_models.payload_content.tool_option import ToolCall, ToolDefinitionI
 from src.services.llm_service import LLMServiceClient
 
 from .builtin_tools import get_builtin_tools
-from .context_messages import AssistantMessage, LLMContextMessage, SessionBackedMessage
+from .context_messages import AssistantMessage, LLMContextMessage, SessionBackedMessage, ToolResultMessage
 from .message_adapter import format_speaker_content
 from .prompt_cli_renderer import PromptCLIVisualizer
 
@@ -609,6 +609,7 @@ class MaisakaChatLoopService:
 
         selected_indices.reverse()
         selected_history = [chat_history[index] for index in selected_indices]
+        selected_history = MaisakaChatLoopService._drop_leading_orphan_tool_results(selected_history)
         return (
             selected_history,
             (
@@ -617,6 +618,36 @@ class MaisakaChatLoopService:
             ),
         )
 
+    @staticmethod
+    def _drop_leading_orphan_tool_results(
+        selected_history: List[LLMContextMessage],
+    ) -> List[LLMContextMessage]:
+        """移除窗口前缀中缺少对应 tool_call 的工具结果消息。"""
+
+        if not selected_history:
+            return selected_history
+
+        available_tool_call_ids = {
+            tool_call.call_id
+            for message in selected_history
+            if isinstance(message, AssistantMessage)
+            for tool_call in message.tool_calls
+            if tool_call.call_id
+        }
+
+        first_valid_index = 0
+        while first_valid_index < len(selected_history):
+            message = selected_history[first_valid_index]
+            if not isinstance(message, ToolResultMessage):
+                break
+            if message.tool_call_id in available_tool_call_ids:
+                break
+            first_valid_index += 1
+
+        if first_valid_index == 0:
+            return selected_history
+        return selected_history[first_valid_index:]
+
     @staticmethod
     def build_chat_context(user_text: str) -> List[LLMContextMessage]:
         """根据用户输入构造最小对话上下文。