TipRanks

get_crypto_historical

Read-onlyIdempotent

Daily OHLC price history for a cryptocurrency, 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_crypto_quote.

Args:
    symbol: Cryptocurrency symbol (e.g. 'BTCUSD' for Bitcoin)
    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	Crypto symbol, e.g. 'BTCUSD' (Bitcoin).
`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 declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds substantial context beyond annotations: it explains aggregation behavior for long ranges, the meaning of summary fields, the absence of vwap in aggregated bars, and distinguishes windowHigh/Low from 52-week figures. This is a model of transparency.

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 long but structured logically, with the core purpose in the first sentence and important behavioral details in separate paragraphs. Every sentence adds useful information, but it could be tightened (e.g., the example row schema and repeated caution about summary might be condensed) without losing value.

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?

There is no output schema, so the description must explain return values. It thoroughly describes the daily row fields, aggregated bar fields, the summary structure, and how to interpret them. It also covers edge cases like over-long ranges and differences from quote-tool fields. The tool's complexity is fully addressed.

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?

The input schema only documents the symbol parameter; from_date and to_date appear without descriptions and with defaults. The description compensates by explicitly listing all three parameters with explanations (e.g., 'Start date YYYY-MM-DD (optional)') and clarifying that the range is 'the whole date range you ask for.' It adds meaning beyond the schema, though default behavior when dates are omitted is not stated.

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 clearly states the tool provides 'Daily OHLC price history for a cryptocurrency' with a specific resource and scope. It differentiates itself from get_crypto_quote by noting it returns historical data, not just the current level, and also distinguishes from other historical tools by specifying crypto-specific behavior.

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 instructs when to use this tool versus alternatives: 'For the current level alone, call get_crypto_quote.' It also provides guidance on when to request a narrower date range to get daily rows instead of aggregated bars, and clarifies how to use summary for claims like 'starting price' or 'a year ago.' This is exemplary usage guidance.

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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