get_required_reserve_ratio_data

Retrieve required reserve ratio data for analyzing monetary policy and banking regulations in China's A-share market, with customizable date ranges and output formats.

Instructions

Required reserve ratio data.

Input Schema

TableJSON Schema

Name	Required	Default
`start_date`	No
`end_date`	No
`year_type`	No	0
`limit`	No
`format`	No	markdown

Implementation Reference

src/tools/macroeconomic.py:41-49 (handler)

The primary MCP tool handler for 'get_required_reserve_ratio_data'. It defines the tool schema via type hints and docstring, registers it with @app.tool(), and delegates execution to the use case via run_tool_with_handling for shared error handling.

@app.tool()
def get_required_reserve_ratio_data(start_date: Optional[str] = None, end_date: Optional[str] = None, year_type: str = '0', limit: int = 250, format: str = "markdown") -> str:
    """Required reserve ratio data."""
    return run_tool_with_handling(
        lambda: fetch_required_reserve_ratio_data(
            active_data_source, start_date=start_date, end_date=end_date, year_type=year_type, limit=limit, format=format
        ),
        context="get_required_reserve_ratio_data",
    )

mcp_server.py:55-55 (registration)
Top-level registration call that invokes the macroeconomic tools registration, including this tool.
```
register_macroeconomic_tools(app, active_data_source)
```

src/use_cases/macroeconomic.py:23-28 (helper)

Use case helper that performs validation, fetches raw data from the data source interface, adds metadata, and formats the output as markdown table.

def fetch_required_reserve_ratio_data(data_source: FinancialDataSource, *, start_date: Optional[str], end_date: Optional[str], year_type: str, limit: int, format: str) -> str:
    validate_output_format(format)
    validate_year_type_reserve(year_type)
    df = data_source.get_required_reserve_ratio_data(start_date=start_date, end_date=end_date, year_type=year_type)
    meta = {"dataset": "required_reserve_ratio", "start_date": start_date, "end_date": end_date, "year_type": year_type}
    return format_table_output(df, format=format, max_rows=limit, meta=meta)

src/baostock_data_source.py:672-676 (helper)

Concrete data source implementation that calls the Baostock query_required_reserve_ratio_data API through a shared macro data fetcher.

def get_required_reserve_ratio_data(self, start_date: Optional[str] = None, end_date: Optional[str] = None, year_type: str = '0') -> pd.DataFrame:
    """Fetches required reserve ratio data using Baostock."""
    # Note the extra yearType parameter handled by kwargs
    return _fetch_macro_data(bs.query_required_reserve_ratio_data, "Required Reserve Ratio", start_date, end_date, yearType=year_type)

src/data_source_interface.py:108-111 (schema)

Interface definition providing the contract (schema) for the data method called by the use case.

@abstractmethod
def get_required_reserve_ratio_data(self, start_date: Optional[str] = None, end_date: Optional[str] = None, year_type: str = '0') -> pd.DataFrame:
    """Fetches required reserve ratio data."""
    pass

A Share MCP