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shigechika

jquants-mcp

by shigechika

get_top_movers

Read-onlyIdempotent

Retrieve top gaining or losing stocks on a specified date. Returns up to 100 stocks with percentage price change.

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
dateYes
directionNoup
nNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds transparency by detailing that split-adjusted closing prices are used, the exact formula for change percentage, and the structure of the return value, which is beyond what annotations provide.

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 well-structured with clear sections (Args, Returns) and is appropriately sized. Every sentence adds value without redundancy, front-loading the purpose and then detailing parameters and return structure.

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?

Given the tool has an output schema and the description explains return values in detail, it is complete. The description covers purpose, parameters, and return format thoroughly, leaving no gaps for a typical use case.

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?

Schema description coverage is 0%, so the description carries full burden. It thoroughly explains each parameter: date format (YYYY-MM-DD or YYYYMMDD), direction (up/down with default 'up'), and n (1–100, default 10). This adds significant meaning beyond the input schema.

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 states 'Return top stocks by percentage price change on a given trading date,' which is a specific verb+resource+metric. It clearly distinguishes from siblings like 'get_top_volume' or 'get_top_turnover_value' that return different metrics.

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 implies usage for fetching top movers by price change but does not explicitly state when to use this tool versus alternatives like 'get_top_turnover_value' or provide exclusions. No when-not or alternative guidance is given.

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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