parse_excel
Extract and return all data from an Excel file, including multiple sheets, using the MCP Development Framework. Specify the file path to process and retrieve structured content quickly and efficiently.
Instructions
Parses an Excel file and returns its content including all sheets
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| file_path | Yes | Path to the Excel file to parse |
Implementation Reference
- mcp_tool/tools/excel_tool.py:23-91 (handler)The execute method that implements the core logic for parsing Excel files. It uses pandas to read all sheets, converts data to structured JSON (with row/column counts, headers, and records), handles errors for missing/invalid files, and returns formatted JSON as TextContent.async def execute(self, arguments: dict) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]: """解析Excel文件并返回内容""" if "file_path" not in arguments: return [types.TextContent( type="text", text="Error: Missing required argument 'file_path'" )] file_path = arguments["file_path"] # 处理文件路径，支持挂载目录的转换 file_path = self.process_file_path(file_path) if not os.path.exists(file_path): return [types.TextContent( type="text", text=f"Error: File not found at path: {file_path}" )] if not file_path.lower().endswith(('.xlsx', '.xls', '.xlsm')): return [types.TextContent( type="text", text=f"Error: File is not an Excel file: {file_path}" )] try: # 读取Excel文件中的所有sheet excel_file = pd.ExcelFile(file_path) sheet_names = excel_file.sheet_names result = { "file_name": os.path.basename(file_path), "sheet_count": len(sheet_names), "sheets": {} } # 解析每个sheet for sheet_name in sheet_names: df = pd.read_excel(excel_file, sheet_name=sheet_name) # 将DataFrame转换为字典 sheet_data = df.to_dict(orient='records') # 获取列名 columns = df.columns.tolist() # 获取行数和列数 row_count = len(df) column_count = len(columns) result["sheets"][sheet_name] = { "row_count": row_count, "column_count": column_count, "columns": columns, "data": sheet_data } # 将结果转换为JSON字符串，并格式化输出 result_json = json.dumps(result, ensure_ascii=False, indent=2, default=str) return [types.TextContent( type="text", text=result_json )] except Exception as e: return [types.TextContent( type="text", text=f"Error: Failed to parse Excel file: {str(e)}" )]
- mcp_tool/tools/excel_tool.py:12-21 (schema)Input schema definition requiring a 'file_path' string parameter.input_schema = { "type": "object", "required": ["file_path"], "properties": { "file_path": { "type": "string", "description": "Path to the Excel file to parse", } }, }
- mcp_tool/tools/excel_tool.py:7-11 (registration)Tool registration via @ToolRegistry.register decorator, defining the tool name 'parse_excel' and description.@ToolRegistry.register class ExcelTool(BaseTool): """Excel解析工具，用于解析Excel文件内容""" name = "parse_excel" description = "Parses an Excel file and returns its content including all sheets"