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16Coffee

Yahoo Finance MCP Server

by 16Coffee

get_stock_actions

Retrieve historical stock dividend and split data by providing a stock ticker. This tool extracts detailed corporate action records from Yahoo Finance for informed investment analysis.

Instructions

获取股票的分红与拆股历史。

参数说明: ticker: str 股票代码,例如 "AAPL"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYes

Implementation Reference

  • server.py:190-198 (registration)
    Tool registration using @fmp_server.tool decorator, specifying name and description with input schema implied.
    @fmp_server.tool(
        name="get_stock_actions",
        description="""获取股票的分红与拆股历史。
    
    参数说明:
        ticker: str
            股票代码,例如 "AAPL"
    """,
    )
  • The core handler function that retrieves historical stock dividends and splits from the Financial Modeling Prep API using requests and pandas, returning JSON data.
    async def get_stock_actions(ticker: str) -> str:
        """Get stock dividends and stock splits for a given ticker symbol"""
        api_key = os.environ.get("FMP_API_KEY")
        if not api_key:
            return "Error: FMP_API_KEY environment variable not set."
    
        base = "https://financialmodelingprep.com/api/v3"
        try:
            div_resp = requests.get(
                f"{base}/historical-price-full/stock_dividend/{ticker}",
                params={"apikey": api_key},
                timeout=10,
            )
            div_resp.raise_for_status()
            div_df = pd.DataFrame(div_resp.json().get("historical", []))
            split_resp = requests.get(
                f"{base}/historical-price-full/stock_split/{ticker}",
                params={"apikey": api_key},
                timeout=10,
            )
            split_resp.raise_for_status()
            split_df = pd.DataFrame(split_resp.json().get("historical", []))
        except Exception as e:
            return f"Error: getting stock actions for {ticker}: {e}"
    
        return json.dumps(
            {
                "dividends": div_df.to_dict("records"),
                "splits": split_df.to_dict("records"),
            }
        )

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