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get_trend_score

Calculate a stock's trend strength score from -100 to +100 using five technical indicators to identify uptrends, downtrends, or neutral market conditions.

Instructions

Get a graduated trend score (-100 to +100) for a stock.

Combines 5 daily indicators: close vs SMA20 (25%), close vs SMA50 (25%), SMA20 slope (20%), ADX direction (15%), and position in 20-day range (15%).

Classifications: strong_uptrend (>=40), uptrend (>=15), neutral (-15 to +15), downtrend (<=-15), strong_downtrend (<=-40).

Args: ticker: Stock symbol (e.g. AAPL, SPY)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYes

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. It effectively describes the tool's behavior by explaining the scoring range (-100 to +100), the five indicators and their weights, and the classification thresholds. However, it does not mention performance characteristics like rate limits or error handling.

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 appropriately sized and front-loaded, starting with the core purpose, followed by methodology details, classifications, and parameter explanation. Every sentence adds value without redundancy, making it efficient and well-structured.

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 (scoring based on multiple indicators) and the presence of an output schema, the description is complete enough. It thoroughly explains the scoring logic and classifications, and with the output schema handling return values, no additional details are needed for effective use.

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?

The description adds significant meaning beyond the input schema, which has 0% coverage. It explains the 'ticker' parameter with an example (e.g., AAPL, SPY) and clarifies it as a stock symbol, compensating fully for the schema's lack of 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 clearly states the tool's purpose with specific verb ('Get') and resource ('graduated trend score for a stock'), and distinguishes it from siblings by focusing on trend scoring rather than quotes, options, or other analyses. It provides detailed information about the scoring methodology and classifications.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for trend analysis of stocks, but does not explicitly state when to use this tool versus alternatives like get_technical_indicators or compare_tickers. No exclusions or specific contexts are provided, leaving guidance at an implied level.

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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