Returns upcoming dividend and earnings events for tickers.
Args:
tickers: Comma-separated ticker symbols (e.g. "AAPL,MSFT")
from_date: Optional start date (ISO format, e.g. "2026-03-01")
to_date: Optional end date (ISO format, e.g. "2026-06-01")
Returns: dividend dates, earnings dates, and coupon dates per ticker.
| Name | Required | Description | Default |
|---|---|---|---|
| tickers | Yes | ||
| to_date | No | ||
| from_date | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly/idempotent/non-destructive, and the description adds meaningful context about the returned event types (dividend, earnings, coupon) and date parameter semantics. No contradiction 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a compact, front-loaded docstring with a one-line summary followed by clear Args and Returns sections. Every sentence provides necessary information with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only tool with no output schema, the description is complete: it explains what the tool returns (dividend, earnings, coupon dates), what inputs are required, and the optional date range format. No critical gaps remain for an agent to invoke it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description compensates by documenting tickers as comma-separated symbols and from_date/to_date as optional ISO-format dates. It adds format and optionality semantics beyond the raw schema, though it does not mention the array alternative for tickers.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Returns upcoming dividend and earnings events for tickers,' using a specific verb, resource, and scope. It distinguishes itself from sibling historical/earnings tools by emphasizing per-ticker upcoming events and coupon dates.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: use for upcoming dividend/earnings/coupon events for specified tickers, with optional date range filtering. It does not explicitly name alternatives or exclusions, but the use case is well implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.