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JerBouma

Finance Toolkit

by JerBouma

volatility

Read-onlyIdempotent

Calculate volatility indicators such as ATR, Bollinger Bands, and True Range for specified tickers, using automatic price data retrieval.

Instructions

Volatility technical indicators (ATR, True Range). Applied to price data automatically — no need to fetch prices first. Requires tickers='AAPL' — use comma-separated values for multiple tickers.

Available indicators: get_bollinger_bands, get_true_range, get_average_true_range, get_supertrend, get_keltner_channels, get_donchian_channels, get_volatility_cone.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lagNoNumber of periods to lag when computing growth rates.
growthNoReturn period-over-period growth rates instead of absolute values.
periodNoObservation frequency, e.g. 'monthly', 'quarterly', or 'annual'.daily
windowNoValue for window. Leave unset to use the default of the indicator you selected. Defaults are 14 for get_average_true_range, get_bollinger_bands, get_keltner_channels; 10 for get_supertrend; 20 for get_donchian_channels.
tickersNoComma-separated ticker symbols, e.g. 'AAPL,MSFT,GOOGL'.
windowsNoValue for windows.
end_dateNoEnd of the date range in YYYY-MM-DD format.2026-08-19
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.
atr_windowNoValue for atr_window.
multiplierNoValue for multiplier.
start_dateNoStart of the date range in YYYY-MM-DD format.2021-08-20
num_std_devNoValue for num_std_dev.
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.
close_columnNoValue for close_column.Adj Close
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.
atr_multiplierNoValue for atr_multiplier.
benchmark_tickerNoTicker used as the market benchmark, e.g. 'SPY' or '^GSPC'.SPY

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=true, so the safety profile is covered. The description adds that price data is fetched automatically and that omitting the indicator returns the list of available indicators, which is useful. However, it doesn't disclose details like rate limits, pagination, or what happens with invalid tickers, but given the annotations, a 3 is appropriate.

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

Conciseness4/5

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

The description is concise and front-loaded with the core purpose. It uses a short paragraph and a bullet-like list of indicators. It avoids unnecessary fluff and provides essential usage hints. Slightly more structure could be added, but it's efficient.

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 the tool's complexity (18 parameters, 7 indicators) and the presence of an output schema, the description is reasonably complete. It covers the key usage points: automatic price fetching, required tickers, indicator list, and window defaults. It doesn't explain return values, but the output schema covers that. It could mention the 'show_columns' parameter for reducing output, but that's in the schema. Overall, adequate for the complexity.

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 schema already documents all parameters. The description adds context for the 'indicator' parameter (required, omitting returns list) and the 'tickers' parameter (comma-separated, required). It also explains the 'window' parameter defaults for different indicators. This adds value beyond the schema, but the schema already does most of the work, so a 3 is appropriate.

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 computes volatility technical indicators (ATR, True Range) and lists the available indicators. It distinguishes itself from siblings by focusing on volatility metrics, though it doesn't explicitly contrast with other technical analysis tools like momentum or overlap.

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

Usage Guidelines4/5

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

The description provides clear usage guidance: it states that price data is fetched automatically (no need to fetch prices first), requires tickers, and lists available indicators. It also mentions that omitting the indicator returns the list of available indicators. However, it doesn't explicitly state when to use this tool versus alternatives like momentum or overlap.

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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