Returns upcoming and recent IPOs.
Args:
fromDate: Start date ISO format (default: today)
toDate: End date ISO format (default: 30 days from now)
| Name | Required | Description | Default |
|---|---|---|---|
| toDate | No | ||
| fromDate | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the default date range (today to 30 days from now) and that it includes both upcoming and recent IPOs, but does not disclose pagination, output format, or any rate limits. This is adequate but not 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 concise and front-loaded with the primary action. The 'Args:' section neatly lists the parameters with concise explanations, using no unnecessary words. It is both compact and well-structured.
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 tool with no output schema, the description gives a high-level return statement but does not describe the structure of the returned IPO data (e.g., company names, dates, exchange). Annotations and parameter explanations cover most of the needed context, but a bit more detail on return values would make it 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?
The schema has no descriptions for parameters, so the description fully compensates by explaining fromDate as 'Start date ISO format' and toDate as 'End date ISO format' with defaults. This adds clear semantic meaning beyond the bare schema fields, turning empty-string defaults into meaningful behavioral specifications.
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 and recent IPOs," which specifies the resource and action. This distinguishes it from sibling tools like get_earnings_calendar or get_economic_calendar by the specific domain (IPO vs. other event types).
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?
Usage is implied by the name and description but not explicitly stated. There is no mention of when to use this tool versus alternatives like get_earnings_calendar or get_economic_calendar, nor any exclusions. The date-range defaults provide some context for typical use.
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.