MCP Yahoo Finance

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get_income_statement

Retrieve income statement data for stock symbols to analyze company financial performance over yearly or quarterly periods.

Instructions

Get income statement for a given stock symbol.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`symbol`	Yes	Stock symbol in Yahoo Finance format.
`freq`	No	At what frequency to get cashflow statements. Defaults to "yearly". Valid freqencies: "yearly", "quarterly", "trainling"

Implementation Reference

src/mcp_yahoo_finance/server.py:260-270 (handler)

MCP tool handler for 'get_income_statement', registered via @mcp_instance.tool() decorator. Delegates to the YahooFinance instance method.

def get_income_statement(
    symbol: str, freq: Literal["yearly", "quarterly", "trainling"] = "yearly"
) -> str:
    """Get income statement 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"
    """
    return yf_instance.get_income_statement(symbol, freq)

src/mcp_yahoo_finance/server.py:104-122 (helper)

YahooFinance class helper method implementing the core logic: fetches income statement using yfinance Ticker.get_income_stmt(), processes DataFrame columns to dates, and returns JSON string.

def get_income_statement(
    self, symbol: str, freq: Literal["yearly", "quarterly", "trainling"] = "yearly"
) -> str:
    """Get income statement 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)
    income_statement = stock.get_income_stmt(freq=freq, pretty=True)

    if isinstance(income_statement, pd.DataFrame):
        income_statement.columns = [
            str(col.date()) for col in income_statement.columns
        ]
        return f"{income_statement.to_json()}"
    return f"{income_statement}"

Schema Changelog

Changes observed during successful MCP inspections.

First observedAug 15, 2025

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves data ('Get'), implying a read-only operation, but doesn't specify whether it requires authentication, has rate limits, returns real-time or historical data, or handles errors. For a data-fetching 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 with zero waste. It's front-loaded with the core purpose, making it easy to parse quickly. Every word earns its place, and there's no redundant or verbose language, achieving optimal conciseness.

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 tool's complexity (financial data retrieval), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the income statement includes (e.g., revenue, expenses), the data format returned, or any limitations (e.g., symbol availability, time ranges). For a tool with no structured output information, this leaves significant gaps for an AI agent.

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 description adds no parameter semantics beyond what the input schema provides. The schema has 100% description coverage, clearly documenting both parameters (symbol and freq) with details like valid frequencies and defaults. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate or add extra meaning.

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 as 'Get income statement for a given stock symbol,' which includes a specific verb ('Get') and resource ('income statement'). It distinguishes from siblings like get_cashflow and get_historical_stock_prices by specifying the financial statement type. However, it doesn't explicitly contrast with all siblings (e.g., get_earning_dates), keeping it from a perfect score.

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 when to prefer get_income_statement over other financial data tools like get_cashflow or get_historical_stock_prices, nor does it specify any prerequisites or exclusions. This lack of contextual direction limits its utility for an AI agent.

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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