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MCP Yahoo Finance

by maxscheijen

get_stock_price_by_date

Retrieve the stock price for a specific symbol on a chosen date using Yahoo Finance data. Input the stock symbol and date in YYYY-MM-DD format for accurate results.

Instructions

Get the stock price for a given stock symbol on a specific date.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYesThe date in YYYY-MM-DD format.
symbolYesStock symbol in Yahoo Finance format.

Implementation Reference

  • Core handler function that implements the logic to fetch the closing stock price for a given symbol and date using yfinance library.
    def get_stock_price_by_date(self, symbol: str, date: str) -> str:
        """Get the stock price for a given stock symbol on a specific date.
    
        Args:
            symbol (str): Stock symbol in Yahoo Finance format.
            date (str): The date in YYYY-MM-DD format.
        """
        stock = Ticker(ticker=symbol, session=self.session)
        price = stock.history(start=date, period="1d")
        return f"{price.iloc[0]['Close']:.4f}"
  • Registration of all tools including get_stock_price_by_date via generate_tool in the MCP list_tools handler.
    @server.list_tools()
    async def list_tools() -> list[Tool]:
        return [
            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),
            generate_tool(yf.get_news),
            generate_tool(yf.get_recommendations),
            generate_tool(yf.get_option_expiration_dates),
            generate_tool(yf.get_option_chain),
        ]
  • Dispatch handler in MCP call_tool that invokes the get_stock_price_by_date method with arguments and returns the result as TextContent.
    case "get_stock_price_by_date":
        price = yf.get_stock_price_by_date(**args)
        return [TextContent(type="text", text=price)]
  • Helper function generate_tool that dynamically generates the JSON schema for the tool based on function signature, type annotations, and docstring.
    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)
  • Supporting utility to parse Google-style docstrings for parameter descriptions used in schema generation.
    def parse_docstring(docstring: str) -> dict[str, str]:
        """Parses a Google-style docstring to extract parameter descriptions."""
        descriptions = {}
        if not docstring:
            return descriptions
    
        lines = docstring.split("\n")
        current_param = None
    
        for line in lines:
            line = line.strip()
            if line.startswith("Args:"):
                continue
            elif line and "(" in line and ")" in line and ":" in line:
                param = line.split("(")[0].strip()
                desc = line.split("):")[1].strip()
                descriptions[param] = desc
                current_param = param
            elif current_param and line:
                descriptions[current_param] += " " + line.strip()
    
        return descriptions
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's function but lacks details on error handling, data sources (e.g., Yahoo Finance implied by schema but not confirmed), rate limits, or authentication needs. For a read operation 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 uses minimal words to convey the essential action and inputs, making it easy for an agent to parse quickly. Every word earns its place, with no redundancy or fluff.

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 low complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks behavioral context and usage guidelines. Without annotations or output schema, the description should ideally add more about return values or error cases, but it's sufficient for a simple lookup tool.

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?

The schema description coverage is 100%, with clear descriptions for both parameters ('date' and 'symbol'). The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. Given the high schema coverage, a baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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: 'Get the stock price for a given stock symbol on a specific date.' It specifies the verb ('Get'), resource ('stock price'), and key constraints ('on a specific date'). However, it doesn't explicitly differentiate from siblings like 'get_current_stock_price' or 'get_historical_stock_prices', which would require a 5.

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 siblings like 'get_current_stock_price' (for current prices) or 'get_historical_stock_prices' (for multiple dates), leaving the agent to infer usage from context alone. This lack of explicit comparison or exclusion criteria limits its utility.

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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