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get_volatility_scan

[$0.05 per call] News Gurus Intel API — VolatilityScanner per-symbol ATR%/volume squeeze scoring from the bot's live volatility_scores table: symbol, atr_pct, volume_usd, galaxy_score, composite_score, asset_class and rank. Optional ?asset_class= filter, ?limit= (default 25, cap 50), ordered by composite_score DESC. Proprietary News Guru computation. Educational data, not financial advice. HOW TO PAY: an x402-capable client settles the payment challenge automatically (USDC on Base, no account needed); wallet-less clients pass a subscriber API key instead (Authorization: Bearer , X-API-Key header, or ?api_key= query) for calls within their plan. Browse every tool + price first with the FREE get_catalog tool.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
asset_classNo

TDQS

A4.3/5.0
Behavior4/5

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

There are no annotations, so the description carries the transparency burden. It discloses pricing, payment methods (x402 or API key), proprietary computation, educational disclaimer, and connection to a live table. This is substantial behavioral context beyond the schema, though it doesn't state potential side effects (unlikely for a read-only listing tool).

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?

The description is a single dense paragraph but front-loaded with purpose and key fields. Payment details and disclaimers are essential but could be shorter. Still, all content serves a purpose for an API tool with authentication nuances.

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?

The tool is relatively simple (2 optional params, no output schema). The description covers purpose, fields, ordering, filters, limits, payment, and disclaimers. Missing explicit return format (envelope vs bare array) but output schema is absent, so the description could have clarified that, a minor gap.

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?

Schema coverage is 0%, so description must compensate. It does: explains limit default and cap (50) and asset_class filter purpose. It adds an ordering note (composite_score DESC) and mention of optional query parameters, helping the agent construct calls. Slight gap: no explicit value format for asset_class.

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 explicitly states it returns per-symbol ATR%/volume squeeze scoring with fields (symbol, atr_pct, volume_usd, galaxy_score, composite_score, asset_class, rank) and ordering (composite_score DESC). It names the underlying table and clearly distinguishes itself from siblings by product and fields.

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?

It mentions optional filters (asset_class, limit) and default/cap, but does not explicitly say when to use this tool over others. However, given sibling names like get_anomalies, get_signals, get_market_regime, the description's focus on volatility squeeze makes use-case inferable, though direct alternatives are not named.

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

B3.4/5.0
Disambiguation5/5

Every tool targets a unique resource or data feed, from agent status and macro snapshots to Polymarket whale analytics and MLB props. There is no overlap or ambiguity between tools, even those within the same domain (e.g., the multiple Polymarket tools are clearly distinguished by their focus on landscape, stats, new wallets, leaders, and flagged whales).

Naming Consistency5/5

The naming follows a consistent get_<resource> pattern for all 35 data retrieval tools, with only verify_memecoin deviating but still using a clear verb-noun structure. The pattern is uniform and predictable, making it easy for an agent to infer the purpose of any tool.

Tool Count2/5

With 36 tools, this significantly exceeds the typical well-scoped range of 3-15. While the server covers a broad range of market intelligence domains, the sheer number of tools makes navigation and selection challenging for an agent, placing it in the 'too many' category.

Completeness4/5

The API provides comprehensive coverage across signals, sentiment, on-chain data, institutional activity, sports, and macro, with both broad aggregate tools and per-symbol/asset specifics. Minor gaps exist, such as a lack of direct news headlines or a fear-greed index, but these are not critical dead ends given the stated purpose of delivering derived intelligence.

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