MCP Development Framework

import os import pandas as pd import json import mcp.types as types from . import BaseTool, ToolRegistry @ToolRegistry.register class ExcelTool(BaseTool): """Excel解析工具，用于解析Excel文件内容""" name = "excel" description = "Parses an Excel file and returns its content including all sheets" input_schema = { "type": "object", "required": ["file_path"], "properties": { "file_path": { "type": "string", "description": "Path to the Excel file to parse", } }, } async def execute(self, arguments: dict) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]: """解析Excel文件内容""" if "file_path" not in arguments: return [types.TextContent( type="text", text="Error: Missing required argument 'file_path'" )] file_path = arguments["file_path"] # 检查文件是否存在 if not os.path.exists(file_path): return [types.TextContent( type="text", text=f"Error: File not found at path: {file_path}" )] # 检查文件扩展名 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)}" )]