TipRanks

get_buybacks

Read-onlyIdempotent

Returns share-buyback / stock-repurchase activity in one of two modes:

- CROSS-MARKET (no ticker): latest reported buybacks across all companies,
  sorted by dollar amount spent (largest first). Use for 'biggest buybacks',
  'top repurchase companies'. A single company may appear multiple times for
  different fiscal quarters.
- PER-TICKER (ticker provided): historical quarterly buyback series for one
  ticker. Use for 'AAPL buyback history', 'MSFT repurchase trend'.

Args:
    ticker: Optional. If provided, returns the per-ticker historical series.
            If empty, returns the cross-market list.
    limit: Cross-market mode only — max rows (default: 25, max: 100).
    page: Cross-market mode only — page number, 1-based (default: 1).

Returns:
    Cross-market mode: { totalCount, data: [{ ticker, companyName,
                         fiscalPeriodEndDate, stockEarningsDate, eps,
                         marketCapUSD, totalValueSpentToRepurchaseShares,
                         epsCurrencyTypeCode }] }.
    Per-ticker mode: quarterly time series, chart-ready as a bar or
        line plot of buyback spend over time —
        [{ date, marketCapEndFiscalPeriod,
           totalValueSpentToRepurchaseShares,
           ratio (decimal — buyback / market cap) }, ...].

Input Schema

TableJSON Schema

Name	Required	Description
`page`	No
`limit`	No
`ticker`	No	Optional. If provided, returns the per-ticker historical buyback series; if omitted, returns the cross-market list.

TDQS

A4.8/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, which the description does not contradict. Adds useful behavioral context: cross-market results are sorted by dollar amount, a company may appear multiple times for different fiscal quarters, and per-ticker returns are chart-ready time series.

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 well-structured with clear sections for modes, arguments, and returns. Every sentence adds value, and the formatting makes it easy to parse.

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?

Since there is no output schema, the description provides comprehensive return field details for both modes, including example field names and the chart-ready nature of per-ticker data. It covers all parameters and operational modes thoroughly.

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?

Description fully explains each parameter beyond the schema. It clarifies that limit and page are cross-market-only with defaults and max, and that ticker determines mode. This compensates for the sparse 33% schema description coverage.

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?

States 'Returns share-buyback / stock-repurchase activity' with two distinct modes, making the tool's purpose clear. It clearly distinguishes from sibling get_* tools by focusing specifically on buyback/repurchase data.

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 provides example queries for each mode: 'biggest buybacks', 'top repurchase companies' for cross-market, and 'AAPL buyback history', 'MSFT repurchase trend' for per-ticker. Also specifies when to omit versus provide ticker, giving clear directional 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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