TipRanks

get_forex_historical

Read-onlyIdempotent

Daily OHLC price history for a forex pair, 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 an exchange-rate 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 rate", "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_forex_quote.

`volume` on a forex row is a per-venue tick count, not market turnover — FX is
over-the-counter. Read it as a liquidity hint at best.

Args:
    symbol: Forex pair symbol (e.g. 'EURUSD')
    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	Forex pair, uppercase with no slash, e.g. 'EURUSD'.
`to_date`	No
`from_date`	No

TDQS

A4.9/5.0

Behavior5/5

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

Beyond the readOnly/destructive annotations, the description discloses aggregation behavior (coarser OHLC bars for long ranges), missing vwap in aggregated bars, summary always covering the full window, and volume being a tick count rather than turnover. No contradictions 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.

Conciseness5/5

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

Though longer than typical descriptions, it is well-structured with clear paragraphs: overview, chart-ready, aggregation, summary, volume caveat, and args. Every sentence adds useful context, and it front-loads the primary purpose. No fluff or repetition.

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 return fields, aggregation behavior, summary structure, and volume semantics. It also covers edge cases like vwap absence and 52-week vs window high/low. The tool is complex, and the description provides enough context for correct invocation and interpretation.

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 only 33%, so description must compensate. The Args section provides format and optionality for from_date and to_date ('YYYY-MM-DD (optional)') and symbol example ('EURUSD'), adding meaning beyond the sparse schema. However, it doesn't explain default behavior when dates are omitted or validation rules.

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 'Daily OHLC price history for a forex pair, covering the whole date range you ask for,' which clearly states the tool's purpose and scope. It also distinguishes it from siblings by explicitly naming get_forex_quote for current levels, and the sibling context shows historical variants for other asset classes.

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?

The description provides explicit usage guidance: 'For the current level alone, call get_forex_quote' and 'Re-request a narrower from_date/to_date for daily rows.' It also advises interpreting summary fields correctly, such as distinguishing windowHigh/windowLow from 52-week figures from quote tools.

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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