Skip to main content
Glama
liqiongyu

Xueqiu MCP

by liqiongyu

top_holders

Retrieve top ten shareholder data for Chinese stocks to analyze ownership structure and investment decisions using Xueqiu MCP's stock market API.

Instructions

获取十大股东数据

Args:
    stock_code: 股票代码
    circula: 只获取流通股,默认为1

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stock_codeNoSZ000002
circulaNo

Implementation Reference

  • main.py:205-214 (handler)
    The main handler function for the 'top_holders' MCP tool. It fetches top shareholders data for a given stock code using the pysnowball library (ball.top_holders), optionally filtered to circulating shares, processes the timestamps, and returns the data as a dictionary.
    @mcp.tool()
    def top_holders(stock_code: str="SZ000002", circula: int = 1) -> dict:
        """获取十大股东数据
        
        Args:
            stock_code: 股票代码
            circula: 只获取流通股,默认为1
        """
        result = ball.top_holders(symbol=stock_code, circula=circula)
        return process_data(result)
  • main.py:34-61 (helper)
    Helper function used by the top_holders tool (and others) to process the raw data, primarily 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 helper function called by process_data to recursively convert timestamp fields in the data to formatted 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
  • main.py:508-510 (registration)
    The MCP server is run here, which registers and exposes all @mcp.tool() decorated functions including top_holders.
    if __name__ == "__main__":
        # This code only runs when the file is executed directly
        mcp.run()
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states what data is retrieved without mentioning whether this is a read-only operation, if it requires authentication, rate limits, error conditions, or what format the data returns. For a data retrieval tool with zero annotation coverage, this is insufficient behavioral context.

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 appropriately concise with a clear purpose statement followed by parameter explanations. The two-sentence structure is efficient, though the parameter section could be slightly better formatted. Every sentence adds value without unnecessary elaboration.

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, no output schema, and 2 parameters, the description is incomplete. It explains what data is retrieved and parameters but doesn't cover return format, error handling, authentication needs, or how this differs from similar tools like 'holders'. For a data retrieval tool in a financial context, more completeness is needed.

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 description provides meaningful parameter information beyond the schema: it explains that 'stock_code' is for stock codes and 'circula' controls whether to get circulating shares only (with default 1). Since schema description coverage is 0%, this description compensates well by adding semantic context that the schema lacks through its titles and defaults alone.

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 clearly states the tool's purpose as '获取十大股东数据' (Get top ten shareholder data), which is a specific verb+resource combination. However, it doesn't explicitly differentiate this tool from the 'holders' sibling tool, which appears to be related. The purpose is clear but lacks sibling differentiation.

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 like the 'holders' tool. It mentions parameters but gives no context about appropriate use cases, prerequisites, or when other tools might be more suitable. This leaves the agent without usage direction.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/liqiongyu/xueqiu_mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server