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JerBouma

Finance Toolkit

by JerBouma

models

Read-onlyIdempotent

Calculate pre-computed financial models (WACC, DuPont, Z-Score, etc.) for given tickers. Choose an indicator to retrieve the computed metric.

Instructions

Pre-computed financial models (WACC, DuPont analysis, Extended DuPont analysis, Enterprise value breakdown, intrinsic value/DCF, Gordon Growth Model, Altman Z-Score, Piotroski F-Score, Beneish M-Score, Economic Value Added (EVA), Present Value of Growth Opportunities, Sustainable Growth Rate, Internal Growth Rate, Graham Number). Requires tickers='AAPL' — use comma-separated values for multiple tickers. Supports quarterly=true and start_date/end_date.

Available indicators: get_altman_z_score, get_beneish_m_score, get_dupont_analysis, get_economic_value_added, get_enterprise_value_breakdown, get_extended_dupont_analysis, get_gorden_growth_model, get_graham_number, get_internal_growth_rate, get_intrinsic_valuation, get_piotroski_score, get_present_value_of_growth_opportunities, get_sustainable_growth_rate, get_weighted_average_cost_of_capital.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lagNoNumber of periods to lag when computing growth rates.
growthNoReturn period-over-period growth rates instead of absolute values.
dilutedNoValue for diluted.
periodsNoValue for periods.
tickersNoComma-separated ticker symbols, e.g. 'AAPL,MSFT,GOOGL'.
end_dateNoEnd of the date range in YYYY-MM-DD format.2026-07-14
trailingNoTrailing window size in number of periods. Sums the raw values over the trailing N periods (e.g. trailing=4 on quarterly data gives a trailing-4-quarter / TTM-style sum) instead of returning one value per period.
indicatorYesName of the specific metric to calculate, e.g. 'get_asset_turnover_ratio'. Required — omitting it returns the list of available indicators.
quarterlyNoReturn quarterly data instead of annual when True.
start_dateNoStart of the date range in YYYY-MM-DD format.2021-07-15
growth_rateNoAssumed constant growth rate as a decimal.
standardizeNoReturn the Z-Score (standard score) instead of the raw values, i.e. how many standard deviations each value is from the mean of its own series. When combined with growth=True, the growth values are standardized instead of the raw values.
show_columnsNoComma-separated names to filter the output. For historical data use the key names visible in any response record (e.g. 'Close,Volume,Return'). For financial statements use the 'metric' field values from the response (e.g. 'Revenue,Net Income,EBITDA'). Call the tool once without this parameter to see all available names, then repeat with show_columns to reduce response size and token usage.
cash_flow_typeNoValue for cash_flow_type.Free Cash Flow
rate_of_returnNoValue for rate_of_return.
calculate_dailyNoValue for calculate_daily.
project_periodsNoValue for project_periods.
benchmark_tickerNoTicker used as the market benchmark, e.g. 'SPY' or '^GSPC'.SPY
include_dividendsNoValue for include_dividends.
show_full_resultsNoValue for show_full_results.
perpetual_growth_rateNoTerminal (perpetual) growth rate used in DCF models.
weighted_average_cost_of_capitalNoWACC as a decimal, e.g. 0.09 for 9 %.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint. The description adds behavioral context like requiring tickers, supporting quarterly and date filtering, and that omitting indicator returns a list. This adds value beyond annotations without contradiction.

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

Conciseness3/5

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

The description is somewhat lengthy and mixes model listing with parameter examples. It lacks clear structure, with run-on sentences. Could be more concise and front-loaded with purpose.

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

Completeness4/5

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

Given 22 parameters, 100% schema coverage, output schema present, and annotations, the description covers the main use case and key parameters. It does not detail every model's behavior, but the indicator names and output schema suffice. Adequate for a read-only data retrieval tool.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds value by listing all indicator names (a quick reference) and reiterating key parameters like tickers and date formats. It does not duplicate schema but provides a helpful overview, especially for the indicator parameter.

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 that the tool provides pre-computed financial models and lists many models (WACC, DuPont, etc.). It is specific about the resource and action, though it could be more concise. Siblings like 'valuation' might overlap but are not addressed, so it slightly lacks differentiation.

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

Usage Guidelines3/5

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

The description gives usage examples (e.g., tickers='AAPL', supports quarterly/date ranges) but does not explicitly state when to use this tool vs. alternatives. It implies its use for financial models but lacks guidance on exclusions or prerequisites.

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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