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find_breakouts

Identify S&P 500 stocks with strong bullish momentum by scanning for price breakouts above key moving averages and optimal RSI levels.

Instructions

Scan S&P 500 top 100 for breakout candidates — stocks in strong uptrends with bullish EMA stacks.

Finds stocks the V3 breakout engine should be watching. Filters for:

  • Strong trend score (default >= 30)

  • RSI in momentum range (45-75, not overbought or oversold)

  • Price above both 50 and 200 EMA (bullish structure)

  • Sorted by trend score (strongest first)

Args: exclude_symbols: Comma-separated symbols to exclude (e.g. existing watchlist) min_trend_score: Minimum trend score (default 30) min_rsi: Minimum RSI (default 45) max_rsi: Maximum RSI — avoid overbought (default 75) max_results: Max results to return (default 15)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
exclude_symbolsNo
min_trend_scoreNo
min_rsiNo
max_rsiNo
max_resultsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure and does so well. It describes the tool's scanning behavior, filtering logic, sorting method, and default values. It doesn't mention rate limits, authentication needs, or error conditions, but provides substantial operational context for a read-only scanning tool.

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 perfectly structured and front-loaded: purpose statement first, then filtering criteria, then parameter details. Every sentence earns its place with no wasted words. The bulleted format for filters and parameter explanations enhances readability without sacrificing conciseness.

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?

Given the tool's complexity (5 parameters, filtering logic, sorting) and the presence of an output schema (which handles return values), the description is complete. It covers purpose, usage context, behavioral details, and parameter semantics thoroughly. The only potential gap is error handling, but that's reasonable given the output schema exists.

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?

With 0% schema description coverage, the description fully compensates by providing detailed semantic explanations for all 5 parameters. Each parameter gets clear context about its purpose, default values, and usage examples (like 'e.g. existing watchlist' for exclude_symbols). This adds significant value beyond the bare schema.

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 clearly states the tool's purpose with specific verbs ('scan', 'finds') and resources ('S&P 500 top 100', 'breakout candidates', 'stocks in strong uptrends with bullish EMA stacks'). It distinguishes itself from siblings by focusing specifically on breakout scanning rather than general analysis, quotes, or screening.

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 provides clear context about when to use this tool ('stocks the V3 breakout engine should be watching'), but doesn't explicitly mention when NOT to use it or name specific alternatives among the sibling tools. The guidance is implied through the specific filtering criteria rather than explicit exclusions.

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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