Returns upcoming earnings dates across all stocks for a date range.
Args:
fromDate: Start date YYYY-MM-DD (default: today)
toDate: End date YYYY-MM-DD (default: 14 days from now)
exchange: Optional exchange filter, e.g. 'US' for US-only results
| Name | Required | Description | Default |
|---|---|---|---|
| toDate | No | ||
| exchange | No | ||
| fromDate | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, which cover safety. The description adds the date default behavior (today to 14 days) but does not disclose additional traits like rate limits, data freshness, or pagination. With annotations present, this is adequate but not extra-rich.
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 two lines plus a concise argument list. The first sentence states the purpose, and the argument list is neatly formatted with defaults and examples. No word wasted.
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?
Despite no output schema, the description tells the agent what to expect: 'upcoming earnings dates'. For a simple filtered-list read-only tool with good annotations, this is sufficient context. The date defaults and exchange filter are documented, making the tool complete.
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?
Schema description coverage is 0%, so the description carries full responsibility. It explains each parameter: fromDate (format YYYY-MM-DD, default today), toDate (format, default 14 days), and exchange (optional, with example 'US'). This fully compensates for the schema gap.
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 the tool returns 'upcoming earnings dates across all stocks for a date range', using a specific verb and resource. It distinguishes from sibling tools like get_ticker_earnings (which is likely stock-specific) and get_economic_calendar (which covers macro events), so purpose clarity is high.
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 scope context ('across all stocks', date range) and the optional exchange filter, implying when to use it. However, it does not explicitly name alternative tools or state when not to use it, earning a 4 rather than a 5.
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.