TipRanks

get_commodity_historical

Read-onlyIdempotent

Daily OHLC price history for a commodity, covering the whole date range you ask for.

Chart-ready: while the range fits in one response, each row is the price feed's
own daily row — {symbol, date, open, high, low, close, volume, change,
changePercent, vwap} — newest first, ordered for direct plotting as a price history.

A range too long to return day by day is aggregated into coarser OHLC bars
rather than cut short. `interval` names which (weekly/monthly/quarterly/yearly),
each bar spans `date` to `endDate`, and a bar's high/low are that period's real
extremes. Aggregated bars carry the same fields except `vwap`, which the feed
defines per session only. Re-request a narrower from_date/to_date for daily rows.

`summary` always describes the FULL requested window, computed from the daily
data: its first and last close with dates, its high and low with dates, and the
trailing changes the window reaches back far enough to support. Base any
"starting price", "a year ago" or "period high/low" claim on `summary`, or on a
bar that is actually present.

`summary.windowHigh`/`windowLow` describe THIS window. A quote tool's
yearHigh/yearLow cover a rolling 52 weeks — a different period — so label those
as 52-week figures. For the current level alone, call get_commodity_quote.

Args:
    symbol: Commodity symbol (e.g. 'GCUSD' for gold)
    from_date: Start date YYYY-MM-DD (optional)
    to_date: End date YYYY-MM-DD (optional)

Input Schema

TableJSON Schema

Name	Required	Description
`symbol`	Yes	Commodity symbol, e.g. 'GCUSD' (gold).
`to_date`	No
`from_date`	No

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already mark this as read-only/idempotent, but the description adds substantial behavior beyond that: automatic aggregation into coarser OHLC bars for long ranges, absence of `vwap` in aggregated bars, chart-ready ordering, and the semantics of `summary` being computed from the full requested window. No contradiction with annotations.

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 longer than average but every section (purpose, aggregation, summary, args) adds unique value. It is front-loaded with the core purpose and uses paragraph breaks effectively, though the interval explanation could be tightened.

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

Completeness5/5

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

With no output schema, the description fully explains the return shape (fields, ordering, aggregation), the meaning of `summary`, and important edge cases like the 52-week distinction. It is complete for a tool of this complexity.

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 low (33%), but the description compensates by documenting all three args with formats ('YYYY-MM-DD') and an example symbol. However, it also references an `interval` concept that does not appear in the input schema, which could confuse—though it does explain how interval affects bar structure.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific action and resource: 'Daily OHLC price history for a commodity, covering the whole date range you ask for.' It clearly distinguishes itself from the sibling quote tool by noting 'For the current level alone, call get_commodity_quote.'

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

Usage Guidelines5/5

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

Explicitly names an alternative for current prices ('For the current level alone, call get_commodity_quote'), advises requesting a narrower date range for daily rows, and clarifies when to rely on `summary` for claims like 'a year ago'. It also warns against conflating window high/low with 52-week quote fields.

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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TDQS

A3.8/5.0

Disambiguation3/5

Many tools have overlapping purposes, e.g. get_etf_analysis vs get_etf_forecast both provide ETF analyst consensus, get_etf_holdings vs get_etf_top_stocks both list constituents, and get_portfolio_overview vs get_portfolio_performance both return returns/performance. The detailed descriptions help, but the sheer number of similar tools creates ambiguity in selection.

Naming Consistency4/5

The set is largely consistent with a 'get_' prefix and descriptive nouns (get_stock_quotes, get_crypto_quote, get_dividend_history). Minor deviations include 'list_my_portfolios' instead of 'get_my_portfolios' and singular/plural variants like get_all_commodities_quotes vs get_commodity_quote, but the pattern remains predictable.

Tool Count1/5

With 71 tools, the count far exceeds the 50+ threshold described as an extreme mismatch. Even though the server covers a broad financial domain, such a large surface is overwhelming for an agent and includes many redundant or highly specific tools that could be consolidated.

Completeness5/5

The tool set provides comprehensive coverage of TipRanks data: quotes and historical data for all major asset classes, news, earnings and economic calendars, analyst and sentiment data, financial statements, technical analysis, options, portfolios, and screeners. There are no obvious dead ends for typical financial research tasks.

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