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get_stock_splits

Read-onlyIdempotent

Returns the stock-split calendar with split ratios and direction (Forward / Reverse). Use for upcoming splits, reverse-split alerts, historical split lookup.

Args:
    fromDate: Start date YYYY-MM-DD (default: 30 days ago)
    toDate: End date YYYY-MM-DD (default: 30 days from now)
    type: Optional filter — 'Forward' or 'Reverse' (case-insensitive).
          Empty string returns both.
    limit: Max rows (default: 25, max: 100)
    ticker: Optional single symbol. Widen fromDate to search a company's history —
            the default window is only today ± 30 days.

Returns: { totalCount, data: [{ ticker, companyName, effectiveDate, type,
                                splitRatio, splitRatioText }] }. A filtered call
         (ticker or type) also returns `matched` and `window`: `totalCount` is
         every split in the window, `matched` how many passed the filter. The
         window is read in full before filtering, so an empty result means there
         genuinely are none rather than that the first page held none.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoOptional filter: 'Forward' or 'Reverse' (case-insensitive); omit to return both.
limitNo
tickerNoOptional. Return only this company's splits within the window, e.g. 'NVDA'. Pair with a wide fromDate for 'has X ever split'.
toDateNo
fromDateNo

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, and non-destructive, and the description adds substantial behavioral context beyond them: default date windows, limit cap, ticker caveat, and the important detail that the window is read in full before filtering so an empty result is meaningful. No contradiction exists.

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?

The description is front-loaded with purpose and use cases, followed by a clean Args block and a returns explanation. The length is justified by the tool's filtering semantics and low schema coverage; every sentence contributes actionable information rather than padding.

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 and sparse input schema, the description supplies a complete invocation picture: defaults, filters, max rows, return object shape, and interpretation of filtered results. Nothing an agent needs to call this correctly or interpret its response is missing.

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?

Schema coverage is only 40%, so the description must compensate — and it does completely. It documents every parameter with format, defaults, and behavior, including YYYY-MM-DD formats, default date ranges, type case-insensitivity, limit range, and ticker-specific guidance about widening fromDate. This adds real meaning beyond the sparse schema.

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?

Description opens with a specific verb and resource: returns the stock-split calendar with split ratios and direction (Forward / Reverse). It also names concrete use cases — upcoming splits, reverse-split alerts, historical lookup — which clearly distinguishes it from calendar-like sibling tools.

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 states explicit use cases ('Use for upcoming splits, reverse-split alerts, historical split lookup'), giving an agent clear context for when to invoke it. It does not explicitly state when not to use it or name alternatives, so it misses the higher bar of exclusion 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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