diff --git a/toolorchestratoolsdata_processor.py b/toolorchestratoolsdata_processor.py
new file mode 100644
index 0000000..b54c278
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+++ b/toolorchestratoolsdata_processor.py
@@ -0,0 +1,87 @@
+"""
+数据处理工具示例
+符合ToolInterface接口规范
+"""
+from typing import Dict, Any
+import pandas as pd
+import numpy as np
+from ..core.orchestrator import ToolInterface, LogMixin
+
+class DataProcessorTool(ToolInterface, LogMixin):
+    """数据处理工具"""
+    
+    def __init__(self):
+        self._name = "data_processor"
+        self._version = "1.0.0"
+        
+    @property
+    def name(self) -> str:
+        return self._name
+        
+    @property
+    def version(self) -> str:
+        return self._version
+        
+    def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
+        """执行数据处理"""
+        try:
+            self.logger.info(f"开始数据处理，参数: {params}")
+            
+            # 输入验证
+            data = params.get("data", [])
+            operation = params.get("operation", "clean")
+            
+            if not data:
+                raise ValueError("输入数据为空")
+                
+            # 转换为DataFrame
+            df = pd.DataFrame(data)
+            
+            # 执行不同操作
+            if operation == "clean":
+                result = self._clean_data(df)
+            elif operation == "analyze":
+                result = self._analyze_data(df)
+            elif operation == "transform":
+                result = self._transform_data(df)
+            else:
+                raise ValueError(f"不支持的操作: {operation}")
+                
+            self.logger.info("数据处理完成")
+            return {
+                "status": "success",
+                "operation": operation,
+                "result": result,
+                "rows_processed": len(df)
+            }
+            
+        except Exception as e:
+            self.logger.error(f"数据处理失败: {str(e)}")
+            raise
+            
+    def _clean_data(self, df: pd.DataFrame) -> Dict:
+        """数据清洗"""
+        # 移除空值
+        df_clean = df.dropna()
+        # 重置索引
+        df_clean = df_clean.reset_index(drop=True)
+        
+        return {
+            "cleaned_data": df_clean.to_dict(orient="records"),
+            "removed_rows": len(df) - len(df_clean)
+        }
+        
+    def _analyze_data(self, df: pd.DataFrame) -> Dict:
+        """数据分析"""
+        numeric_cols = df.select_dtypes(include=[np.number]).columns
+        
+        analysis = {}
+        for col in numeric_cols:
+            analysis[col] = {
+                "mean": float(df[col].mean()),
+                "std": float(df[col].std()),
+                "min": float(df[col].min()),
+                "max": float(df[col].max())
+            }
+            
+        return {"analysis": analysis}
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