A Share MCP

Instructions

Implementation Reference

• src/tools/macroeconomic.py:33-39 (handler)

  The primary MCP tool handler for 'get_loan_rate_data'. It is decorated with @app.tool() for automatic registration and delegates execution to the use case layer with standardized error handling via run_tool_with_handling.

  @app.tool()
  def get_loan_rate_data(start_date: Optional[str] = None, end_date: Optional[str] = None, limit: int = 250, format: str = "markdown") -> str:
      """Benchmark loan rates."""
      return run_tool_with_handling(
          lambda: fetch_loan_rate_data(active_data_source, start_date=start_date, end_date=end_date, limit=limit, format=format),
          context="get_loan_rate_data",
      )

• mcp_server.py:55-55 (registration)

  The call to register_macroeconomic_tools which defines and registers the macroeconomic tools, including get_loan_rate_data, to the FastMCP app instance.

  register_macroeconomic_tools(app, active_data_source)

• src/use_cases/macroeconomic.py:16-20 (helper)

  Use case helper that fetches loan rate data from the data source, applies validation and formatting, and returns formatted output.

  def fetch_loan_rate_data(data_source: FinancialDataSource, *, start_date: Optional[str], end_date: Optional[str], limit: int, format: str) -> str:
      validate_output_format(format)
      df = data_source.get_loan_rate_data(start_date=start_date, end_date=end_date)
      meta = {"dataset": "loan_rate", "start_date": start_date, "end_date": end_date}
      return format_table_output(df, format=format, max_rows=limit, meta=meta)

• src/data_source_interface.py:104-105 (schema)

  Abstract method in FinancialDataSource interface defining the expected signature for loan rate data retrieval.

  def get_loan_rate_data(self, start_date: Optional[str] = None, end_date: Optional[str] = None) -> pd.DataFrame:
      """Fetches benchmark loan rates."""

• src/baostock_data_source.py:668-670 (helper)

  Concrete implementation in BaostockDataSource that fetches loan rate data via Baostock API using the shared _fetch_macro_data helper.

  def get_loan_rate_data(self, start_date: Optional[str] = None, end_date: Optional[str] = None) -> pd.DataFrame:
      """Fetches benchmark loan rates using Baostock."""
      return _fetch_macro_data(bs.query_loan_rate_data, "Loan Rate", start_date, end_date)