Skip to main content
Glama
RedSamerai

a-stock-mcp

by RedSamerai

Scan Limit Down Stocks

scan_limit_down_stocks

Scan today's A-share stocks hitting the daily limit down (price decline ≤ -9.9%) to identify potential weak performers or market stress signals. Returns count and list.

Instructions

扫描今日跌停股列表(涨跌幅 <= -9.9%)。

Returns

dict {"count": int, "results": [...], "error": str | None}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It states the operation is a read-only 'scan', defines the filtering rule, and explicitly gives the return shape as a dict with count, results, and error. It does not describe edge cases or pagination, but for a zero-parameter scanner this is reasonably transparent.

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 compact and front-loaded: the core action and threshold appear first, and the return structure is clearly listed. No redundant filler or unnecessary detail is present.

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 parameterless tool with a simple list-return contract, the description is complete. It tells the agent what will be scanned, the exact threshold, and what the response will look like. There is no additional setup, permission, or prerequisite information an agent would need before invoking it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are zero parameters, so the baseline is 4. The description correctly imposes no parameter expectations and the empty schema is self-explanatory; no additional parameter semantics are needed.

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 uses a specific verb ('scan') tied to a precise resource ('today's limit-down stock list') and defines the threshold (price change <= -9.9%). This clearly distinguishes it from sibling tools like scan_limit_up_stocks, which scans in the opposite direction.

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 intended use is implied: agents needing today's limit-down stocks should call this tool. However, the description does not explicitly mention when to prefer it over alternatives such as a_stock_screener or limit_stats, nor does it state when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.