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shigechika

jquants-mcp

by shigechika

get_top_movers

Read-onlyIdempotent

Find top stock gainers or losers by percentage price change on a trading date. Specify date, direction, and count to get ranked list with closing prices and change percentages.

Instructions

Return top stocks by percentage price change on a given trading date.

Uses split-adjusted closing prices (AdjC) to compute change_pct = (today - prev) / prev * 100.

Args: date: Trading date in YYYY-MM-DD or YYYYMMDD format. direction: "up" for top gainers, "down" for top losers. Default: "up". n: Number of stocks to return (1–100). Default: 10.

Returns: dict with keys: - date: the requested trading date - previous_date: the comparison base date - direction: "up" or "down" - items: list of up to n dicts, each with: - code: stock code (5-digit) - close: today's closing price - prev_close: previous day's closing price - change_pct: percentage price change

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nNo
dateYes
directionNoup

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description supplements this by explaining the split-adjusted price calculation, the formula, and the inclusion of previous_date in the return object. No contradictions and useful extra behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concisely structured with a purpose statement, formula, Args list, and Returns list. Every sentence adds essential information without redundancy or verbosity.

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?

For a read-only market data tool, the description covers input parameters, output keys, data types, and calculation logic. The return format is fully specified, and the context provided by annotations and sibling tool names further enriches the completeness.

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 description coverage, the description fully documents all three parameters: date format (YYYY-MM-DD or YYYYMMDD), direction values ('up'/'down' with default), and n range (1-100 with default). It exactly compensates for the 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?

The description clearly states it returns top stocks by percentage price change on a given trading date, with a specific verb and resource. This distinguishes it from related tools like get_top_volume and get_top_turnover_value.

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?

The description provides clear context for when to use this tool (when needing top gainers/losers by percentage change) but does not explicitly mention alternatives or when-not-to-use. This is sufficient for an agent to infer usage among siblings.

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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