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sacahan

CasualMarket

by sacahan

get_real_time_trading_stats

Retrieve real-time market trading statistics, including total volume, total value, advancing and declining stocks, and top gainers. Data updates every 5 minutes for current market status.

Instructions

取得即時交易統計資訊。

提供市場即時交易狀態,包括當盤成交量、成交金額、 漲跌家數分布等,資料每5分鐘更新一次。

使用範例: get_real_time_trading_stats() # 查詢即時交易統計

Args: 無參數

Returns: MCPToolResponse[TradingStatsData]: 統一格式的回應,包含: - success (bool): 操作是否成功 - data (TradingStatsData): 即時交易統計資訊,包含: * update_time: 資料更新時間 * market_status: 市場狀態 * total_volume: 總成交量 (億股) * total_value: 總成交金額 (億元) * transaction_count: 總成交筆數 * advancing_stocks: 上漲家數 * declining_stocks: 下跌家數 * unchanged_stocks: 平盤家數 * limit_up_stocks: 漲停家數 * limit_down_stocks: 跌停家數 * top_gainers: 漲幅前幾名列表 * top_losers: 跌幅前幾名列表 * active_stocks: 成交量前幾名列表 - error (str): 錯誤訊息(失敗時) - tool (str): 工具名稱

Raises: 查詢失敗時返回錯誤回應,可能的原因: - 非交易時段 - 資料來源暫時無法存取 - 服務暫時異常

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior5/5

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

No annotations are provided, so the description carries full responsibility for behavioral disclosure. It explicitly states the 5-minute update interval, potential failure conditions (非交易時段、資料來源暫時無法存取、服務暫時異常), and the complete response structure. This is thorough transparency for a zero-parameter read operation.

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?

The description is well-organized into Usage, Args, Returns, and Raises sections. The lengthy Returns section is justified because no output schema is present, so all return fields need documentation. The only minor redundancy is rephrasing the purpose in the usage example.

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?

Despite having no output schema, the description documents all return fields, units, wrapper structure, and error scenarios, making external documentation unnecessary. For a zero-parameter read-only tool, this is comprehensive enough for an agent to invoke it correctly.

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?

The tool has zero parameters, and the empty input schema already documents this. The description reinforces it with '無參數' and a usage example, adding confirmation without introducing ambiguity. Since there are no parameters to explain, the baseline score of 4 is appropriate.

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 opens with a specific verb and resource: '取得即時交易統計資訊' and enumerates concrete market-wide metrics such as 當盤成交量、成交金額、漲跌家數分布. This clearly distinguishes the tool from sibling tools that focus on individual stocks, historical data, or index quotes.

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 use case is clear from the word '即時' and the note '資料每5分鐘更新一次', implying this tool is for current market-wide trading statistics. It does not explicitly name alternatives or state when not to use it, but the context is unambiguous enough for selection.

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