Real-time Stock MCP Service

get_real_time_data

Fetch real-time stock data for a given symbol. Returns price, change, and volume in a Markdown table.

Instructions

获取指定股票的实时股票数据，包括价格、涨跌幅、成交量等信息。

Args: symbol: 股票代码，数字后带上交易所代码，格式如688041.SH

Returns: 格式化的实时股票数据，以Markdown表格形式展示

Examples: - get_real_time_data("688041.SH")

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "symbol": "<symbol>"
    },
    "name": "get_real_time_data"
  }
}

Input Schema

Name	Required	Description	Default
`symbol`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.2.1May 17, 2026

TDQS

A3.5/5.0

Behavior2/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It states the return format (Markdown table) but omits details such as data freshness, error handling, authentication needs, or whether the operation has side effects. For a read operation, basic safety is implied but not stated.

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 fairly concise with a short sentence plus structured Args/Returns/Examples sections. It avoids unnecessary details, though the example could be integrated into the text. The structure aids readability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one parameter and an output schema, the description provides essential context: purpose, return format, and parameter format. However, it lacks details on edge cases, error messages, or data freshness. Given the low complexity, it is mostly complete.

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 schema has 0% parameter descriptions, but the description adds crucial format guidance: '股票代码，数字后带上交易所代码，格式如688041.SH'. This clarifies the exact expected input format beyond the schema's bare type string.

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 the verb '获取' (get) and resource '实时股票数据' (real-time stock data), specifying key fields like price, change, and volume. It distinguishes from siblings such as get_kline (historical) and get_market_performance (broader).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. The example shows a usage pattern, but there is no mention of prerequisites, exclusions, or contexts where other tools would be preferred. With many siblings, this lack of guidance may lead to confusion.

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