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liqiongyu

Xueqiu MCP

by liqiongyu

holders

Retrieve shareholder count data for Chinese stocks to analyze ownership structure and investor interest. Query stock codes to access F10 shareholder information through Xueqiu MCP.

Instructions

获取F10股东人数数据

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stock_codeNoSZ000002

Implementation Reference

  • main.py:224-228 (handler)
    The handler function implementing the 'holders' MCP tool. Decorated with @mcp.tool() for registration. Calls ball.holders(stock_code) to fetch F10 shareholder count data and processes it with process_data before returning as dict.
    @mcp.tool()
    def holders(stock_code: str="SZ000002") -> dict:
        """获取F10股东人数数据"""
        result = ball.holders(stock_code)
        return process_data(result)
  • main.py:34-61 (helper)
    Shared helper function used by the 'holders' tool (and others) to process raw data from the ball library, primarily converting timestamps to 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 helper utility called by process_data to recursively convert timestamp fields in the data to 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 the full burden of behavioral disclosure. It only states what data is fetched without any information on permissions, rate limits, data freshness, or response format. For a data retrieval tool with zero annotation coverage, this is inadequate and fails to disclose critical behavioral traits.

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 that directly states the tool's purpose without any fluff or redundancy. It is appropriately sized and front-loaded, making it easy to parse quickly.

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 complexity of financial data tools, lack of annotations, no output schema, and minimal parameter documentation, the description is incomplete. It does not cover behavioral aspects, usage context, or return values, leaving significant gaps for an AI agent to understand how to invoke it correctly.

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

Parameters3/5

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

The description does not mention parameters at all, and the input schema has 0% description coverage, with only one parameter 'stock_code' documented structurally. Since there is only one parameter, the baseline is higher, but the description adds no semantic value beyond the schema, such as explaining what 'F10' refers to or format expectations for the stock code.

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

Purpose4/5

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

The description '获取F10股东人数数据' clearly states the action ('获取' meaning 'get' or 'fetch') and the resource ('F10股东人数数据' meaning 'F10 shareholder count data'), providing a specific purpose. However, it does not explicitly differentiate from sibling tools like 'top_holders' or 'org_holding_change', which might also relate to shareholder information, so it lacks sibling distinction.

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?

The description provides no guidance on when to use this tool versus alternatives. It does not mention any context, prerequisites, or exclusions, such as whether it's for real-time data, historical trends, or specific stock types. With many sibling tools potentially overlapping in financial data, this omission is significant.

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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