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leoncuhk

MCP Yahoo Finance

by leoncuhk

get_cashflow

Retrieve cash flow statements for stocks to analyze financial health and liquidity. Specify symbol and frequency (yearly, quarterly) for detailed cash flow data.

Instructions

Get cashflow for a given stock symbol.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesStock symbol in Yahoo Finance format.
freqNoAt what frequency to get cashflow statements. Defaults to "yearly". Valid freqencies: "yearly", "quarterly", "trainling"

Implementation Reference

  • MCP tool handler function for get_cashflow, registered via @mcp_instance.tool() decorator, delegates to YahooFinance.get_cashflow
    @mcp_instance.tool()
    def get_cashflow(
        symbol: str, freq: Literal["yearly", "quarterly", "trainling"] = "yearly"
    ) -> str:
        """Get cashflow for a given stock symbol.
    
        Args:
            symbol (str): Stock symbol in Yahoo Finance format.
            freq (str): At what frequency to get cashflow statements. Defaults to "yearly".
                    Valid freqencies: "yearly", "quarterly", "trainling"
        """
        # Note: Original function didn't specify return type, assuming str
        return str(yf_instance.get_cashflow(symbol, freq))
  • Supporting helper method in YahooFinance class that fetches and formats cashflow data using yfinance.
    def get_cashflow(
        self, symbol: str, freq: Literal["yearly", "quarterly", "trainling"] = "yearly"
    ):
        """Get cashflow for a given stock symbol.
    
        Args:
            symbol (str): Stock symbol in Yahoo Finance format.
            freq (str): At what frequency to get cashflow statements. Defaults to "yearly".
                    Valid freqencies: "yearly", "quarterly", "trainling"
        """
        stock = Ticker(ticker=symbol, session=self.session)
        cashflow = stock.get_cashflow(freq=freq, pretty=True)
    
        if isinstance(cashflow, pd.DataFrame):
            cashflow.columns = [str(col.date()) for col in cashflow.columns]
            return f"{cashflow.to_json(indent=2)}"
        return f"{cashflow}"

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 for behavioral disclosure. It only states the basic action without mentioning data freshness, rate limits, authentication needs, or error handling. For a financial data tool, this lack of context on reliability and constraints is a significant gap.

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 any fluff or redundancy. It's appropriately sized and front-loaded, making it easy to parse quickly.

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 no annotations and no output schema, the description is incomplete. It doesn't explain what the cashflow data includes (e.g., operating, investing, financing activities), format of return values, or any prerequisites. For a financial data retrieval tool, this leaves critical gaps in understanding the tool's behavior and output.

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%, so the input schema fully documents both parameters ('symbol' and 'freq'). The description adds no additional meaning beyond implying cashflow is retrieved per stock symbol, which is already clear from the schema. Baseline 3 is appropriate as the schema handles parameter documentation.

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 ('cashflow for a given stock symbol'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its siblings like 'get_income_statement' or 'get_dividends', which are also financial data retrieval tools for stocks, so it misses full sibling distinction.

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 siblings like 'get_income_statement' and 'get_dividends' available, it doesn't specify scenarios where cashflow data is preferred over other financial metrics, leaving the agent without context for tool selection.

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