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TipRanks

by com.tipranks

get_earnings_history

Read-onlyIdempotent

Quarterly earnings time series for a ticker. Chart-ready: each quarter row is {period, report_date, actual_eps, estimate_eps, eps_surprise_pct, prior_year_eps, eps_yoy_change_pct, actual_revenue, estimate_revenue, revenue_surprise_pct, net_income, ...}, ordered oldest-first so a bar chart of actual vs. estimate EPS, or a YoY trend line, plots directly.

Also returns next_quarter — the upcoming scheduled report with the
consensus estimate, low/high estimate band, and expected report date
— for forward-looking charts.

Use for: "AAPL earnings history", "earnings surprise trend", "did
NVDA beat last quarter", "EPS beat/miss the past 4 quarters".

Args:
    ticker: Stock ticker (e.g. 'AAPL', 'NVDA').
    quarters: Number of most-recent reported quarters to return
               (default 8, max 40).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYes
quartersNo

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; the description adds chart-readiness, oldest-first ordering, field lists, and next_quarter metadata, enriching the behavioral contract with no contradictions. This is a strong addition beyond structured 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 longer than average but well-organized: overview, data shape, next_quarter, use cases, and args. No fluff; each section adds distinct value, and the structure 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?

With no output schema, the description fully describes the return rows and ordering, making it self-contained for an agent to predict output. It also covers the optional quarters parameter behavior, leaving little ambiguity.

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 input schema only lists types and a default; the description compensates by documenting both parameters, specifying ticker format and the quarters default (8) and maximum (40), filling the 0% schema coverage gap completely.

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 opens with 'Quarterly earnings time series for a ticker,' a specific verb+resource statement, and later lists concrete use cases like 'AAPL earnings history' and 'EPS beat/miss the past 4 quarters,' clearly distinguishing it from sibling earnings tools like get_earnings_calendar.

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 'Use for' section provides explicit query examples that signal when to invoke this tool, but it does not name alternatives or exclusion criteria, so it falls short of the full 'when-not' guidance. The examples are enough to place it in context, but explicit exclusions would push to 5.

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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