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liqiongyu

Xueqiu MCP

by liqiongyu

industry_compare

Compare F10 industry data for Chinese stocks to analyze sector performance and identify investment opportunities.

Instructions

获取F10行业对比数据

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stock_codeNoSZ000002

Implementation Reference

  • main.py:251-255 (handler)
    The handler function decorated with @mcp.tool(), implementing the industry_compare tool. It calls the pysnowball library's industry_compare method and processes the result with process_data.
    @mcp.tool()
    def industry_compare(stock_code: str="SZ000002") -> dict:
        """获取F10行业对比数据"""
        result = ball.industry_compare(stock_code)
        return process_data(result)
  • main.py:34-61 (helper)
    Helper function used by industry_compare (and other tools) to process the raw data, including converting timestamps to readable datetime strings.
    def process_data(data, process_config=None):
        """
        通用数据处理函数,可扩展添加各种数据处理操作
        
        Args:
            data: 原始数据
            process_config: 处理配置字典,用于指定要执行的处理操作
                例如: {'convert_timestamps': True, 'other_process': params}
        
        Returns:
            处理后的数据
        """
        if process_config is None:
            # 默认配置
            process_config = {
                'convert_timestamps': True
            }
        
        # 如果开启了时间戳转换
        if process_config.get('convert_timestamps', True):
            data = convert_timestamps(data)
        
        # 在这里可以添加更多的数据处理逻辑
        # 例如:
        # if 'format_numbers' in process_config:
        #     data = format_numbers(data, **process_config['format_numbers'])
        
        return data
  • main.py:14-31 (helper)
    Supporting utility called by process_data to recursively convert timestamp fields in the data to human-readable datetime strings.
    def convert_timestamps(data):
        """递归地将数据中的所有 timestamp 转换为 datetime 字符串"""
        if isinstance(data, dict):
            for key, value in list(data.items()):
                if key == 'timestamp' and isinstance(value, (int, float)) and value > 1000000000000:  # 毫秒级时间戳
                    data[key] = datetime.datetime.fromtimestamp(value/1000).strftime('%Y-%m-%d %H:%M:%S')
                elif key == 'timestamp' and isinstance(value, (int, float)) and value > 1000000000:  # 秒级时间戳
                    data[key] = datetime.datetime.fromtimestamp(value).strftime('%Y-%m-%d %H:%M:%S')
                elif key.endswith('_date') and isinstance(value, (int, float)) and value > 1000000000000:  # 毫秒级时间戳
                    data[key] = datetime.datetime.fromtimestamp(value/1000).strftime('%Y-%m-%d %H:%M:%S')
                elif key.endswith('_date') and isinstance(value, (int, float)) and value > 1000000000:  # 秒级时间戳
                    data[key] = datetime.datetime.fromtimestamp(value).strftime('%Y-%m-%d %H:%M:%S')
                elif isinstance(value, (dict, list)):
                    data[key] = convert_timestamps(value)
        elif isinstance(data, list):
            for i, item in enumerate(data):
                data[i] = convert_timestamps(item)
        return data
Behavior1/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It only states the action ('获取' - get) without any details on permissions, rate limits, data freshness, or output format. For a tool with no annotations, this is a significant gap, offering no behavioral context beyond the basic purpose.

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 a single, efficient sentence in Chinese ('获取F10行业对比数据') with no wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every element contributes directly to stating the purpose.

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

Completeness2/5

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

Given the tool has no annotations, 0% schema description coverage, and no output schema, the description is incomplete. It doesn't explain what 'F10' refers to, what 'industry comparison' entails, or how the parameter influences the output. For a tool with such minimal structured data, more context is needed to be fully usable.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate. It mentions 'F10行业对比数据' but doesn't explain the 'stock_code' parameter's role in this context (e.g., whether it's the target stock for comparison or a reference). No additional parameter details are provided, failing to address the coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description '获取F10行业对比数据' (Get F10 industry comparison data) states a general purpose but lacks specificity. It mentions 'F10' (likely a financial data source) and 'industry comparison', but doesn't clarify what exactly is compared (e.g., metrics, companies) or the scope. It's vague about the verb '获取' (get) - whether it retrieves, calculates, or generates data. No distinction from siblings is provided.

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 guidance on when to use this tool versus alternatives is given. The description doesn't mention prerequisites, context, or exclusions. With many sibling tools (e.g., 'balance', 'income', 'indicator'), there's no indication of how this tool differs or when it's appropriate, leaving usage entirely implicit.

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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