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marckwei

MCP Yahoo Finance

by marckwei

get_historical_stock_prices

Retrieve historical stock price data for analysis by specifying a stock symbol, time period, and interval. Use this tool to access past market performance from Yahoo Finance.

Instructions

Get historical stock prices for a given stock symbol.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesStock symbol in Yahoo Finance format.
periodNoThe period for historical data. Defaults to "1mo". Valid periods: "1d", "5d", "1mo", "3mo", "6mo", "1y", "2y", "5y", "10y", "ytd", "max"
intervalNoThe interval beween data points. Defaults to "1d". Valid intervals: "1d", "5d", "1wk", "1mo", "3mo"

Implementation Reference

  • The core handler function in the YahooFinance class that retrieves and formats historical stock prices using yfinance's Ticker.history method.
    def get_historical_stock_prices(
        self,
        symbol: str,
        period: Literal[
            "1d", "5d", "1mo", "3mo", "6mo", "1y", "2y", "5y", "10y", "ytd", "max"
        ] = "1mo",
        interval: Literal["1d", "5d", "1wk", "1mo", "3mo"] = "1d",
    ) -> str:
        """Get historical stock prices for a given stock symbol.
    
        Args:
            symbol (str): Stock symbol in Yahoo Finance format.
            period (str): The period for historical data. Defaults to "1mo".
                    Valid periods: "1d", "5d", "1mo", "3mo", "6mo", "1y", "2y", "5y", "10y", "ytd", "max"
            interval (str): The interval beween data points. Defaults to "1d".
                    Valid intervals: "1d", "5d", "1wk", "1mo", "3mo"
        """
        stock = Ticker(ticker=symbol, session=self.session)
        prices = stock.history(period=period, interval=interval)
    
        if hasattr(prices.index, "date"):
            prices.index = prices.index.date.astype(str)  # type: ignore
        return f"{prices['Close'].to_json(orient='index')}"
  • Registers all tools including 'get_historical_stock_prices' by generating Tool objects from the YahooFinance methods using generate_tool.
    @server.list_tools()
    async def list_tools() -> list[Tool]:
        return [            
            generate_tool(yf.cmd_run),
            generate_tool(yf.get_recommendations),
            generate_tool(yf.get_news),
            generate_tool(yf.get_current_stock_price),
            generate_tool(yf.get_stock_price_by_date),
            generate_tool(yf.get_stock_price_date_range),
            generate_tool(yf.get_historical_stock_prices),
            generate_tool(yf.get_dividends),
            generate_tool(yf.get_income_statement),
            generate_tool(yf.get_cashflow),
            generate_tool(yf.get_earning_dates),
        ]
  • The dispatch logic in server.call_tool() that matches the tool name and invokes the corresponding handler.
    case "get_historical_stock_prices":
        price = yf.get_historical_stock_prices(**args)
        return [TextContent(type="text", text=price)]
  • Utility function that inspects the handler function to generate the MCP Tool schema, including input schema from type annotations and docstring descriptions.
    def generate_tool(func: Any) -> Tool:
        """Generates a tool schema from a Python function."""
        signature = inspect.signature(func)
        docstring = inspect.getdoc(func) or ""
        param_descriptions = parse_docstring(docstring)
    
        schema = {
            "name": func.__name__,
            "description": docstring.split("Args:")[0].strip(),
            "inputSchema": {
                "type": "object",
                "properties": {},
            },
        }
    
        for param_name, param in signature.parameters.items():
            param_type = (
                "number"
                if param.annotation is float
                else "string"
                if param.annotation is str
                else "string"
            )
            schema["inputSchema"]["properties"][param_name] = {
                "type": param_type,
                "description": param_descriptions.get(param_name, ""),
            }
    
            if "required" not in schema["inputSchema"]:
                schema["inputSchema"]["required"] = [param_name]
            else:
                if "=" not in str(param):
                    schema["inputSchema"]["required"].append(param_name)
    
        return Tool(**schema)
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves historical data but doesn't describe what the return format looks like (e.g., time series data, JSON structure), any rate limits, authentication needs, or error handling. For a data retrieval tool with zero annotation coverage, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose without unnecessary details. It wastes no words and is appropriately sized for a straightforward data retrieval tool, earning a high score for conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (retrieving historical data with three parameters) and no output schema, the description is minimally complete but lacks details on return values or behavioral traits. It covers the basic purpose but doesn't compensate for the absence of annotations or output schema, making it adequate but with clear gaps for agent usability.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with all parameters ('symbol', 'period', 'interval') well-documented in the input schema, including defaults and valid values. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 for adequate but not enhanced parameter information.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Get') and resource ('historical stock prices'), making it easy to understand what it does. However, it doesn't explicitly distinguish this tool from sibling tools like 'get_current_stock_price' or 'get_stock_price_date_range', which also retrieve stock price data but with different temporal scopes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_current_stock_price' for real-time data or 'get_stock_price_date_range' for custom date ranges, leaving the agent to infer usage based on tool names alone without explicit context or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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