TipRanks

get_index_historical

Read-onlyIdempotent

Daily OHLC price history for a major market index, 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 index 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 level", "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_index_quote.

Args:
    symbol: Index symbol (e.g. '^GSPC' for S&P 500)
    from_date: Start date YYYY-MM-DD
    to_date: End date YYYY-MM-DD (defaults to today)

Input Schema

TableJSON Schema

Name	Required	Description
`symbol`	Yes	Index symbol, e.g. '^GSPC' for the S&P 500.
`to_date`	No
`from_date`	Yes

TDQS

A4.9/5.0

Behavior5/5

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

The description goes far beyond the annotations by detailing aggregation behavior for long ranges, the omission of vwap in aggregated bars, the summary always describing the full requested window, and the distinction between windowHigh/windowLow and 52-week figures. No contradiction with readOnlyHint or idempotentHint.

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 lengthy but well-structured with paragraphs and an Args section. Every sentence provides necessary detail about return behavior and edge cases, though it could be slightly tightened. The core purpose is front-loaded.

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?

Given there is no output schema, the description thoroughly explains return fields, aggregation, summary semantics, and caveats like vwap only per session. It anticipates confusion with quote-based 52-week figures and provides complete guidance for agent use.

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

Parameters5/5

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

The schema has only 33% description coverage, so the description compensates by explaining all three parameters with examples (symbol '^GSPC'), date format (YYYY-MM-DD), and defaults (to_date defaults to today). It adds meaning beyond the bare schema fields.

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 it returns daily OHLC price history for a major market index over a requested date range. It distinguishes itself from get_index_quote by explicitly noting that for current level alone, one should call get_index_quote, and it differentiates from other historical tools by focusing on index symbols.

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?

Provides explicit usage guidance: when to use this tool vs get_index_quote, when to re-request a narrower range to get daily rows, and how to interpret summary vs quote-based figures. It clearly explains behavior for long ranges and directs the agent on how to handle data presentation.

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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