TipRanks

get_dividend_history

Read-onlyIdempotent

Dividend payment history for a ticker. Chart-ready: includes the current snapshot (yield %, payout ratio, frequency, annualised payout) plus two time series — a per-payment list [{ex_date, pay_date, amount, yield_pct, is_special, ...}] (newest first, capped at count) and an annual_totals list [{year, amount, yield_pct}] suitable for a yearly bar chart.

All yields are emitted as percentages (4.5 = 4.5%), so plots don't
need to know which underlying field used decimal vs. percentage
encoding.

Use for: "AAPL dividend history", "yield trend over 5 years",
"dividend growth chart", "is the payout sustainable".

Args:
    ticker: Stock ticker (e.g. 'AAPL', 'JNJ').
    count: Number of most-recent individual payments to return
            (default 16, max 100). The annual_totals series is
            always returned in full.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`count`	No
`ticker`	Yes

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already indicate read-only/idempotent, and the description adds significant behavioral context: output structure, yield percentage format (4.5 = 4.5%), per-payment list capped at 'count', and annual_totals always returned in full. 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?

The description is well-organized: purpose first, then output structure, then yield encoding, use cases, and argument details. Every sentence adds value, and the formatting with headings/paragraphs makes it easy to scan.

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 no output schema, the description thoroughly explains the return shape (snapshot, per-payment list, annual totals) and edge behavior (count cap, yield percentages). It sufficiently covers the tool's functionality for an agent to select and invoke it correctly.

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 0% with no descriptions for ticker or count. The description compensates by explaining ticker with examples and count with default (16) and max (100), plus the effect on annual_totals. This adds meaningful semantics beyond the 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?

The description explicitly states 'Dividend payment history for a ticker' and details the output components (current snapshot, per-payment list, annual totals). This clearly distinguishes it from siblings like get_stock_splits or get_earnings_history.

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?

Provides clear usage examples ('Use for: AAPL dividend history', 'yield trend over 5 years') and describes the nature of the data. However, it does not explicitly mention when not to use this tool or direct users to alternative tools, so it stops short of full 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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