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get_etf_screener

Read-onlyIdempotent

Filters ETFs by asset class, category, and focus.

Args:
    assetClass: Alternatives, AssetAllocation, Commodities, Currency, Equity, FixedIncome
    category: BroadMarket, Sector, HighDividendYield, SizeAndStyle, Corporate, etc.
    focus: LargeCap, SmallCap, MidCap, TotalMarket, Financials, HealthCare,
           InformationTechnology, Energy, RealEstate, HighDividendYield, HighYield, etc.
           Use this for a sector question ('tech ETFs' -> InformationTechnology);
           the screener has no `sector` filter.
    limit: Rows to return (default 20, max 100).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
focusNoFocus filter, e.g. 'LargeCap', 'InformationTechnology', 'HighYield'. This is the screener's SECTOR filter — there is no separate sector parameter.
limitNoRows to return (default: 20, max: 100).
categoryNoCategory filter, e.g. 'Sector', 'BroadMarket', 'HighDividendYield'
assetClassNoAsset class filter, e.g. 'Equity', 'FixedIncome', 'Commodities'

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description goes beyond annotations by adding behavioral details: the default limit of 20 and max of 100, and the semantic quirk that `focus` functions as the sector filter since no dedicated `sector` parameter exists. This is genuinely useful context for correct invocation.

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 compact, front-loaded with the tool's purpose, and organized as a clean Args block. Each parameter line earns its place by adding value beyond the schema, and the sector note is integrated naturally without padding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read-only screening tool with no required parameters, the description covers all parameters, provides valid value hints, and states limits. The only minor gap is that no output schema exists and the description does not describe the return shape, but for a screener the implied result of a filtered ETF list is likely sufficient for an agent to proceed.

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?

Although schema coverage is 100%, the description adds meaning well beyond the schema. It enumerates valid assetClass values, gives representative category examples, and crucially explains how to use `focus` for sector questions, including a concrete example mapping 'tech ETFs' to InformationTechnology. This helps the agent pick correct parameter values where the schema only gives generic descriptions.

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 a specific verb and resource: 'Filters ETFs by asset class, category, and focus.' This clearly identifies the tool as a screening/filtering operation and distinguishes it from sibling ETF tools like get_etf_analysis, get_etf_holdings, and get_etf_exposures by emphasizing screening dimensions rather than retrieving a specific data slice.

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 description gives explicit usage guidance for one common scenario: 'Use this for a sector question' and maps 'tech ETFs' to InformationTechnology. It also states an important exclusion: 'the screener has no `sector` filter.' It doesn't name alternative sibling tools explicitly, but the when-to-use guidance is clear enough for an agent to select this tool over a generic ETF data tool.

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.

Resources