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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)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions 'Get historical stock prices' but doesn't disclose behavioral traits such as rate limits, authentication requirements, data freshness, error handling, or response format. For a tool with no annotation coverage, this leaves significant gaps in understanding how it behaves.

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 directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with every word contributing to understanding what the tool does.

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

Completeness2/5

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

Given the complexity of financial data tools, no annotations, and no output schema, the description is incomplete. It doesn't explain what the return values look like (e.g., data format, structure), nor does it address important context like data sources (Yahoo Finance implied by parameter but not stated), limitations, or error cases. For a tool with 3 parameters and no structured output documentation, more completeness is needed.

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 three parameters ('symbol', 'interval', 'period') well-documented in the schema including defaults and valid values. The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline of 3 for high schema coverage without compensating value.

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 verb 'Get' and resource 'historical stock prices for a given stock symbol', making the purpose unambiguous. However, it doesn't explicitly differentiate from siblings like 'get_stock_price_by_date' or 'get_stock_price_date_range', which likely offer similar functionality with different parameter approaches.

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. With multiple sibling tools related to stock prices (e.g., 'get_current_stock_price', 'get_stock_price_by_date', 'get_stock_price_date_range'), there's no indication of which tool is appropriate for different scenarios like real-time vs. historical data or single-date vs. range queries.

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