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shigechika

jquants-mcp

by shigechika

detect_52w_high_low

Read-onlyIdempotent

Detect stocks breaking 52-week highs or lows on a given trading date. Scan all codes cross-sectionally or target a specific stock, with optional detailed per-stock data.

Instructions

Screen for 52-week rolling high/low breakouts (52週高値/安値 ブレイク). All plans.

Use for 52週高値, 52週安値, 年間高値, 年間安値, 52-week high/low breakout. For multi-date scans use detect_52w_high_low_range (not repeated calls here). For YTD high/low use detect_ytd_high_low instead.

Default params hit the nightly pre-computed cache (sub-second). Custom params or code filter compute on-demand (~10–30s cross-sectional on Cloud Run). date must be within the past 52 weeks. 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). Within past 52 weeks. code: Optional stock code. Omit to scan all codes (cross-sectional). window_sessions: Trailing session window (default 252 = 52 weeks). min_prior_sessions: Drop codes with fewer prior sessions in window (default 60; set 1 to disable). detail: Include full per-stock data array (default False = summary counts only).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYes
codeNo
window_sessionsNo
min_prior_sessionsNo
detailNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Adds significant behavioral context beyond annotations: describes cache vs on-demand computation, typical response times, date constraints, data availability time, and plan restrictions.

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?

Well-structured with purpose, guidelines, performance, and parameter details in order, but slightly verbose with repeated keywords.

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 aspects: purpose, siblings, behavior, parameters, plans, and constraints, despite no output schema details.

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?

With 0% schema coverage, description fully explains all 5 parameters including format, defaults, optionality, and purpose, compensating for missing schema descriptions.

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?

Clearly states it screens for 52-week rolling high/low breakouts, provides synonyms in English and Japanese, and explicitly distinguishes from sibling tools detect_52w_high_low_range and detect_ytd_high_low.

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 guides when to use this tool vs alternatives (multi-date range tool, YTD tool) and provides performance expectations (cache vs on-demand) and 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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