Office MCP Server

"""Excel 数据分析工具.""" from typing import Any, Optional from fastmcp import FastMCP from loguru import logger from office_mcp_server.handlers.excel_handler import ExcelHandler def register_analysis_tools(mcp: FastMCP, excel_handler: ExcelHandler) -> None: """注册 Excel 数据分析工具.""" @mcp.tool() def excel_descriptive_statistics( filename: str, sheet_name: str, data_range: str, output_cell: Optional[str] = None, ) -> dict[str, Any]: """Excel 描述性统计分析.""" logger.info(f"MCP工具调用: excel_descriptive_statistics(filename={filename})") return excel_handler.descriptive_statistics(filename, sheet_name, data_range, output_cell) @mcp.tool() def excel_correlation_analysis( filename: str, sheet_name: str, data_range1: str, data_range2: str, ) -> dict[str, Any]: """Excel 相关性分析 - 计算两组数据的相关系数. Args: filename: 文件名 sheet_name: 工作表名称 data_range1: 第一组数据范围 (如 'A1:A10') data_range2: 第二组数据范围 (如 'B1:B10') """ logger.info(f"MCP工具调用: excel_correlation_analysis(filename={filename})") return excel_handler.correlation_analysis(filename, sheet_name, data_range1, data_range2) @mcp.tool() def excel_goal_seek( filename: str, sheet_name: str, formula_cell: str, target_value: float, variable_cell: str, ) -> dict[str, Any]: """Excel 单变量求解.""" logger.info(f"MCP工具调用: excel_goal_seek(filename={filename})") return excel_handler.goal_seek(filename, sheet_name, formula_cell, target_value, variable_cell) @mcp.tool() def excel_regression_analysis( filename: str, sheet_name: str, y_range: str, x_range: str, output_cell: Optional[str] = None, confidence_level: float = 0.95, ) -> dict[str, Any]: """Excel 回归分析.""" logger.info(f"MCP工具调用: excel_regression_analysis(filename={filename})") return excel_handler.regression_analysis( filename, sheet_name, y_range, x_range, output_cell, confidence_level ) @mcp.tool() def excel_anova( filename: str, sheet_name: str, data_ranges: list[str], output_cell: Optional[str] = None, alpha: float = 0.05, ) -> dict[str, Any]: """Excel 方差分析 (ANOVA).""" logger.info(f"MCP工具调用: excel_anova(filename={filename})") return excel_handler.anova(filename, sheet_name, data_ranges, output_cell, alpha) @mcp.tool() def excel_t_test( filename: str, sheet_name: str, range1: str, range2: str, test_type: str = "two-sample", output_cell: Optional[str] = None, alpha: float = 0.05, ) -> dict[str, Any]: """Excel t检验.""" logger.info(f"MCP工具调用: excel_t_test(filename={filename}, type={test_type})") return excel_handler.t_test(filename, sheet_name, range1, range2, test_type, output_cell, alpha) @mcp.tool() def excel_chi_square_test( filename: str, sheet_name: str, observed_range: str, expected_range: Optional[str] = None, output_cell: Optional[str] = None, ) -> dict[str, Any]: """Excel 卡方检验.""" logger.info(f"MCP工具调用: excel_chi_square_test(filename={filename})") return excel_handler.chi_square_test(filename, sheet_name, observed_range, expected_range, output_cell) @mcp.tool() def excel_trend_analysis( filename: str, sheet_name: str, data_range: str, forecast_periods: int = 5, output_cell: Optional[str] = None, ) -> dict[str, Any]: """Excel 趋势分析.""" logger.info(f"MCP工具调用: excel_trend_analysis(filename={filename}, forecast={forecast_periods})") return excel_handler.trend_analysis(filename, sheet_name, data_range, forecast_periods, output_cell) @mcp.tool() def excel_moving_average( filename: str, sheet_name: str, data_range: str, window_size: int = 3, output_cell: Optional[str] = None, ) -> dict[str, Any]: """Excel 移动平均.""" logger.info(f"MCP工具调用: excel_moving_average(filename={filename}, window={window_size})") return excel_handler.moving_average(filename, sheet_name, data_range, window_size, output_cell) @mcp.tool() def excel_exponential_smoothing( filename: str, sheet_name: str, data_range: str, alpha: float = 0.3, output_cell: Optional[str] = None, ) -> dict[str, Any]: """Excel 指数平滑.""" logger.info(f"MCP工具调用: excel_exponential_smoothing(filename={filename}, alpha={alpha})") return excel_handler.exponential_smoothing(filename, sheet_name, data_range, alpha, output_cell)