A Share MCP

Instructions

Implementation Reference

• src/tools/indices.py:41-47 (handler)

  MCP tool handler for get_hs300_stocks. Decorated with @app.tool(), it invokes the use case via run_tool_with_handling for execution and error handling.
  @app.tool() def get_hs300_stocks(date: Optional[str] = None, limit: int = 250, format: str = "markdown") -> str: """CSI 300 constituents.""" return run_tool_with_handling( lambda: fetch_index_constituents(active_data_source, index="hs300", date=date, limit=limit, format=format), context="get_hs300_stocks", )
• mcp_server.py:53-53 (registration)

  Registration call for index tools, including get_hs300_stocks, by invoking register_index_tools(app, active_data_source).
  register_index_tools(app, active_data_source)
• src/use_cases/indices.py:25-36 (helper)

  Use case helper fetch_index_constituents that routes to data_source.get_hs300_stocks() for HS300 index and formats the output.
  def fetch_index_constituents(data_source: FinancialDataSource, *, index: str, date: Optional[str], limit: int, format: str) -> str: validate_output_format(format) key = validate_index_key(index, INDEX_MAP) if key == "hs300": df = data_source.get_hs300_stocks(date=date) elif key == "sz50": df = data_source.get_sz50_stocks(date=date) else: df = data_source.get_zz500_stocks(date=date) meta = {"index": key, "as_of": date or "latest"} return format_table_output(df, format=format, max_rows=limit, meta=meta)
• src/baostock_data_source.py:574-577 (helper)

  Data source implementation of get_hs300_stocks, delegating to shared _fetch_index_constituent_data helper using Baostock's query_hs300_stocks API.
  def get_hs300_stocks(self, date: Optional[str] = None) -> pd.DataFrame: """Fetches CSI 300 index constituents using Baostock.""" return _fetch_index_constituent_data(bs.query_hs300_stocks, "CSI 300", date)
• src/baostock_data_source.py:78-124 (helper)

  Shared helper function _fetch_index_constituent_data that performs the actual Baostock API query, error handling, and DataFrame construction for index constituents.
  def _fetch_index_constituent_data( bs_query_func, index_name: str, date: Optional[str] = None ) -> pd.DataFrame: logger.info( f"Fetching {index_name} constituents for date={date or 'latest'}") try: with baostock_login_context(): # date is optional, defaults to latest rs = bs_query_func(date=date) if rs.error_code != '0': logger.error( f"Baostock API error ({index_name} Constituents) for date {date}: {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 {index_name} constituent data found for date {date}. Baostock msg: {rs.error_msg}") else: raise DataSourceError( f"Baostock API error fetching {index_name} constituents: {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 {index_name} constituent data found for date {date} (empty result set).") raise NoDataFoundError( f"No {index_name} constituent data found for date {date} (empty result set).") result_df = pd.DataFrame(data_list, columns=rs.fields) logger.info( f"Retrieved {len(result_df)} {index_name} constituents for date {date or 'latest'}.") return result_df except (LoginError, NoDataFoundError, DataSourceError, ValueError) as e: logger.warning( f"Caught known error fetching {index_name} constituents for date {date}: {type(e).__name__}") raise e except Exception as e: logger.exception( f"Unexpected error fetching {index_name} constituents for date {date}: {e}") raise DataSourceError( f"Unexpected error fetching {index_name} constituents for date {date}: {e}")