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veroq_social_sentiment

Analyze social media sentiment for stocks and cryptocurrencies to gauge retail investor buzz and market perception across platforms like Reddit and Twitter.

Instructions

Get social media sentiment for a stock or crypto ticker from Reddit, Twitter/X, and other platforms.

WHEN TO USE: To gauge retail investor sentiment and social buzz around a specific ticker. RETURNS: Overall sentiment score, mention count, per-platform breakdown, trending topics, and top posts with URLs. COST: 2 credits. EXAMPLE: { "symbol": "TSLA" }

Input Schema

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

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

No annotations are provided, so the description carries the full burden. It successfully discloses critical behavioral traits: cost ('2 credits'), return structure ('sentiment score, mention count, per-platform breakdown...'), and data sources. Could mention rate limiting or caching behavior to achieve a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Uses a clear section-based structure (WHEN TO USE, RETURNS, COST, EXAMPLE) that front-loads the core purpose. Highly information-dense with no wasted sentences, though the all-caps headers create a slightly mechanical readability compared to flowing prose.

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 simplicity (single required parameter) and absence of an output schema, the description comprehensively documents the return values and cost structure. No gaps remain for an agent to successfully invoke and interpret results.

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

Parameters4/5

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

While the input schema has 100% coverage with a clear parameter description, the description adds practical value by providing a concrete JSON example ('EXAMPLE: { "symbol": "TSLA" }'), which helps the agent understand the expected input format beyond the raw 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 specific action ('Get social media sentiment'), target resource ('stock or crypto ticker'), and data sources ('Reddit, Twitter/X, and other platforms'), effectively distinguishing it from sibling tools like veroq_ticker_news (news articles) and veroq_social_trending (general trends).

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?

Includes an explicit 'WHEN TO USE' section stating the tool is for gauging 'retail investor sentiment and social buzz around a specific ticker,' providing clear selection criteria. Lacks explicit 'when not to use' guidance or named alternative tools (e.g., vs. veroq_social_trending) that would earn a 5.

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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