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shigechika

jquants-mcp

by shigechika

get_markets_short_ratio

Read-onlyIdempotent

Retrieve short selling ratio for TSE 33 industry sectors. Query by sector code or date range; returns summary or full data.

Instructions

TSE 33-sector short selling ratio (業種別空売り比率). Standard+ only.

Use for 業種別空売り比率, sector-level 空売り動向, industry short selling trends. Keyed by s33 sector code — not per stock. For per-stock institutional short positions (大量空売り残高), use get_markets_short_sale_report instead.

When called with no parameters, returns a compact summary by default (detail=False): {count, latest_date, source, note}. Pass detail=True to retrieve full row data for the latest available date. Specifying any filter parameter (s33, date, etc.) always returns full data.

[Supported plans] Standard / Premium

Args: s33: TSE 33-sector code (e.g. 0050 = Fishery, Agriculture & Forestry) date: Date (YYYYMMDD or YYYY-MM-DD) date_from: Start date for range query date_to: End date for range query detail: When True and no filter params given, return full row data instead of summary.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
s33No
dateNo
date_fromNo
date_toNo
detailNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Discloses default compact summary behavior, detail parameter effect, and filter behavior. Annotations already indicate read-only, so no contradiction. Adds valuable 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?

Well-structured with clear sections, but the Args section is somewhat redundant with the earlier explanation. Still, every sentence adds value and it's logically organized.

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 purpose, usage, behavior, parameters, alternatives, and return shapes (summary vs full). With output schema present, return values need not be detailed. Complete for a tool with multiple behaviors.

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?

Since schema has 0% descriptions, the description fully explains each parameter: s33 with example, date formats, date_from/to range, detail boolean effect. This compensates entirely 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 returns TSE 33-sector short selling ratio, distinguishing it from per-stock short report. Verb 'get' is implied and specific resource is identified.

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 states when to use (sector-level short ratio) and when not (per-stock positions), directing to get_markets_short_sale_report. Also explains behavior with parameters: no params returns summary, filters return full data.

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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