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24mlight

A Share MCP

get_performance_express_report

Generate performance reports for A-share stocks within specified date ranges to analyze financial data and market indicators.

Instructions

Performance express report within date range.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes
start_dateYes
end_dateYes
limitNo
formatNomarkdown

Implementation Reference

  • MCP tool handler for get_performance_express_report. Decorated with @app.tool(), validates inputs via run_tool_with_handling, delegates to use case fetch_performance_express_report.
    @app.tool()
    def get_performance_express_report(code: str, start_date: str, end_date: str, limit: int = 250, format: str = "markdown") -> str:
        """Performance express report within date range."""
        return run_tool_with_handling(
            lambda: fetch_performance_express_report(
                active_data_source, code=code, start_date=start_date, end_date=end_date, limit=limit, format=format
            ),
            context=f"get_performance_express_report:{code}:{start_date}-{end_date}",
        )
  • mcp_server.py:52-52 (registration)
    Registration call for financial reports tools, including get_performance_express_report, in the main MCP server setup.
    register_financial_report_tools(app, active_data_source)
  • Use case helper that fetches data from FinancialDataSource interface, applies formatting and validation.
    def fetch_performance_express_report(data_source: FinancialDataSource, *, code: str, start_date: str, end_date: str, limit: int, format: str) -> str:
        validate_output_format(format)
        df = data_source.get_performance_express_report(code=code, start_date=start_date, end_date=end_date)
        meta = {"code": code, "start_date": start_date, "end_date": end_date, "dataset": "Performance Express"}
        return format_table_output(df, format=format, max_rows=limit, meta=meta)
  • Abstract method definition in FinancialDataSource interface, defining the expected input/output signature for the core data fetching method.
    @abstractmethod
    def get_performance_express_report(self, code: str, start_date: str, end_date: str) -> pd.DataFrame:
        pass
  • Concrete implementation in BaostockDataSource using bs.query_performance_express_report to fetch raw data from Baostock API.
    def get_performance_express_report(self, code: str, start_date: str, end_date: str) -> pd.DataFrame:
        """Fetches performance express reports (业绩快报) using Baostock."""
        logger.info(
            f"Fetching Performance Express Report for {code} ({start_date} to {end_date})")
        try:
            with baostock_login_context():
                rs = bs.query_performance_express_report(
                    code=code, start_date=start_date, end_date=end_date)
    
                if rs.error_code != '0':
                    logger.error(
                        f"Baostock API error (Perf Express) for {code}: {rs.error_msg} (code: {rs.error_code})")
                    if "no record found" in rs.error_msg.lower() or rs.error_code == '10002':
                        raise NoDataFoundError(
                            f"No performance express report found for {code} in range {start_date}-{end_date}. Baostock msg: {rs.error_msg}")
                    else:
                        raise DataSourceError(
                            f"Baostock API error fetching performance express report: {rs.error_msg} (code: {rs.error_code})")
    
                data_list = []
                while rs.next():
                    data_list.append(rs.get_row_data())
    
                if not data_list:
                    logger.warning(
                        f"No performance express report found for {code} in range {start_date}-{end_date} (empty result set).")
                    raise NoDataFoundError(
                        f"No performance express report found for {code} in range {start_date}-{end_date} (empty result set).")
    
                result_df = pd.DataFrame(data_list, columns=rs.fields)
                logger.info(
                    f"Retrieved {len(result_df)} performance express report records for {code}.")
                return result_df
    
        except (LoginError, NoDataFoundError, DataSourceError, ValueError) as e:
            logger.warning(
                f"Caught known error fetching performance express report for {code}: {type(e).__name__}")
            raise e
        except Exception as e:
            logger.exception(
                f"Unexpected error fetching performance express report for {code}: {e}")
            raise DataSourceError(
                f"Unexpected error fetching performance express report for {code}: {e}")

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