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veroq_ticker_score

Calculate a composite trading signal score for stocks using sentiment, momentum, volume, and event data to identify bull/bear signals.

Instructions

Get a composite trading signal score for a ticker based on sentiment, momentum, coverage volume, and event proximity.

WHEN TO USE: For a quick bull/bear signal on a ticker. Use veroq_ticker_analysis for deeper context behind the signal. RETURNS: Composite score, signal (strong_bullish to strong_bearish), and component breakdown (sentiment, momentum, volume, events with weights). COST: 2 credits. EXAMPLE: { "symbol": "NVDA" }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesTicker symbol (e.g. AAPL, NVDA, TSLA)
Behavior4/5

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

No annotations are provided, so the description carries full burden. It successfully discloses cost ('2 credits') and return value structure ('Composite score, signal...component breakdown'). It could be improved by mentioning idempotency or caching behavior, but covers the critical economic and output contract details.

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?

Excellent structure with clear section headers (WHEN TO USE, RETURNS, COST, EXAMPLE). Every sentence earns its place. The main purpose is front-loaded in the first sentence, followed by contextual usage guidance and technical details.

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?

For a single-parameter tool with no output schema, the description is complete. It compensates for the missing output schema by detailing the return structure in the RETURNS section, mentions cost implications, and provides sibling context. No gaps remain for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with the 'symbol' parameter fully documented as 'Ticker symbol (e.g. AAPL, NVDA, TSLA)'. The description provides a JSON example that reinforces usage but does not add semantic meaning beyond what the schema already provides. Baseline 3 is appropriate when schema documentation is complete.

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 ('Get') and resource ('composite trading signal score'), clearly stating the four components used (sentiment, momentum, coverage volume, event proximity). It explicitly distinguishes itself from sibling tool 'veroq_ticker_analysis' by contrasting 'quick bull/bear signal' vs 'deeper context'.

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

Usage Guidelines5/5

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

Contains an explicit 'WHEN TO USE' section stating the specific scenario ('quick bull/bear signal on a ticker') and names the exact alternative tool ('veroq_ticker_analysis') for deeper analysis. This provides clear guidance on tool selection vs siblings.

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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