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shigechika

jquants-mcp

by shigechika

detect_volume_surge

Read-onlyIdempotent

Detect stocks with abnormally high trading volume compared to their average over a baseline period. Filter by date, surge multiplier, and custom baseline days.

Instructions

Identify stocks with abnormally high trading volume (出来高急増) on a given day. All plans.

Use for 出来高急増・出来高異常・売買活況・volume spike queries. surge_ratio = Vo / mean(prior baseline_days). For price extremes use detect_52w/ytd_high_low; for price limits use detect_price_limit. Data available ~17:15 JST on trading days.

[Supported plans] Free / Light / Standard / Premium (cache-only, no API call)

Args: date: Trading date (YYYYMMDD or YYYY-MM-DD). multiplier: surge_ratio threshold (default 2.0). baseline_days: Trailing sessions for baseline average (default 20). code: Optional stock code. Omit to scan all stocks. detail: Include full per-stock data array (default False).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYes
multiplierNo
baseline_daysNo
codeNo
detailNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Annotations already indicate readOnly and idempotent, but description adds that data is cache-only and available at ~17:15 JST, providing useful behavioral context beyond annotations.

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?

Description is somewhat long but well-structured with clear sections; no wasted sentences, though could be slightly more concise.

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?

Covers all necessary context: purpose, usage, parameters, data timing, plan limitations, and caching behavior. Output schema exists so return values are not needed in description.

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?

Despite 0% schema coverage, description thoroughly explains each parameter (date format, multiplier meaning, baseline_days, code optional, detail flag) with defaults and usage context.

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?

Description clearly states it identifies stocks with abnormally high trading volume and explicitly distinguishes from sibling tools like detect_52w_high_low and detect_price_limit.

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?

Explicitly tells when to use this tool for volume spike queries and excludes price-related queries, naming alternative tools. Also provides data availability time.

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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