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bitbankinc

bitbank-lab-mcp

Official
by bitbankinc

get_volatility_metrics

Compute volatility indicators (ATR, realized vol, Parkinson, Garman-Klass, Rogers-Satchell) for crypto pairs with annualized and rolling variants to measure market risk.

Instructions

[Volatility / ATR / RV] ボラティリティ指標(volatility / ATR / realized vol)を算出。RV・ATR・Parkinson・Garman-Klass・Rogers-Satchell。年率換算対応。aggregates.atr は Wilder ATR(RMA ベース、period=14、TradingView・MT4 標準と一致)。ローリングではボラ変化を RV / Parkinson で追跡してください。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tzNoAsia/Tokyo
pairYes
typeYes
viewNoview は content の量を制御します。量は summary < detailed < full の順で、full は常にそのツールの最重量です。view が structuredContent から**フィールドを削ることはありません**(その view でしか計算しないデータを足すツールはあり、その場合は当該 view の説明に明記しています)。content[0].text は LLM への唯一のチャネルなので、軽い view は「短い表示」ではなく「LLM が明細を受け取らない」を意味します。 本ツールの結論は aggregates / rolling(スカラー値)なので、**full でも系列そのものは content に出ない**(系列が必要なら get_candles を使う)。 - summary(既定): 集計 + ローリング分析 + 4 行フッタ。窓ごとの内訳・Assessment は content に出ない。 - detailed: summary + 【Volatility Metrics】/【Rolling Trends】/【Assessment】の内訳。系列の統計値は content に出ない。 - full: detailed + 【Series】(件数 / 期間 / Close レンジ / リターンの平均・標準偏差)。本ツールの最重量。 - beginner(**階梯外**): 平易な日本語 4 行。**専門用語・指標名・フッタは content に出ない**——読者向けレジスタを変える view であり、量の階梯には乗らない。summary
limitNo
windowsNo
annualizeNo
cacheTtlMsNo
useLogReturnsNo
Behavior3/5

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 does reveal important behavioral traits: annualization is supported ('年率換算対応'), aggregates.atr follows the Wilder ATR (RMA-based, period=14, matching TradingView/MT4), and it recommends RV/Parkinson for rolling analysis. However, it does not describe the overall output shape (e.g., whether it returns both aggregates and rolling series), data access side effects, or any rate-limits/auth concerns. It is partially transparent but missing the broader behavioral context an agent needs for a computation 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 compact, with the purpose front-loaded via tags. It covers the essential metric types, annualization, and the ATR variant in a few sentences, and ends with a practical rolling-analysis tip. While slightly redundant ('volatility / ATR / realized vol' followed by the list), it is efficient and structured for quick scanning. It earns a 4 for being appropriately sized and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has no output schema, so the description must explain return values and behavior. It only hints at an 'aggregates.atr' field and mentions rolling, but does not clarify whether the output includes both aggregate scalars and rolling arrays, what the 'view' parameter does (that's in the schema but not cross-referenced), or what the overall structured content looks like. Given 9 parameters and no output schema, this description leaves the agent under-informed about what it will receive and how to interpret the result.

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

Parameters2/5

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

Schema description coverage is only 11% (only the 'view' parameter has a description in the schema), so the description must compensate heavily. The tool description does not explain most of the 9 parameters: pair, type, limit, windows, annualize, useLogReturns, etc. It only indirectly hints at annualize ('年率換算対応') and the rolling concept (related to windows), without mapping them to parameter names. An agent would have to guess the meaning of useLogReturns or windows. This is a significant gap given the low schema coverage.

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 tags [Volatility / ATR / RV] and states a clear verb + resource: 'ボラティリティ指標を算出' (computes volatility metrics). It then enumerates the exact indicators (RV, ATR, Parkinson, Garman-Klass, Rogers-Satchell) and notes annualization support. This specificity makes it immediately distinguishable from generic siblings like analyze_indicators, which cover a broad range of technical indicators.

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 gives internal guidance—'ローリングではボラ変化を RV / Parkinson で追跡してください' (for rolling, track volatility changes with RV/Parkinson)—which is a usage tip for metric selection, but it does not explicitly compare against sibling tools or state when to prefer this tool over alternatives like analyze_indicators or get_flow_metrics. Usage context is implied by the tool's focus on volatility, but no exclusions or alternative routes are provided, leaving the agent to infer when to choose this tool.

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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