Skip to main content
Glama
liqiongyu

Xueqiu MCP

by liqiongyu

org_holding_change

Retrieve institutional holding data for Chinese stocks to analyze investment trends and portfolio changes.

Instructions

获取F10机构持仓数据

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stock_codeNoSZ000002

Implementation Reference

  • main.py:231-236 (handler)
    The primary handler function for the MCP tool 'org_holding_change'. It is registered using the @mcp.tool() decorator from FastMCP. The function fetches institutional holding change data from the pysnowball library (ball.org_holding_change) and processes the timestamps using the shared process_data helper.
    @mcp.tool()
    def org_holding_change(stock_code: str="SZ000002") -> dict:
        """获取F10机构持仓数据"""
        result = ball.org_holding_change(stock_code)
        return process_data(result)
  • main.py:34-62 (helper)
    Shared helper function used by all tools, including org_holding_change, to process data by 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)
    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
Behavior2/5

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. The description only states what data is retrieved ('F10 institutional holding data') but doesn't mention any behavioral traits such as whether this is a read-only operation, potential rate limits, authentication requirements, data freshness, or what format the data is returned in. For a data retrieval tool with zero annotation coverage, this is a significant gap.

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 phrase that directly states the tool's function. There's no wasted verbiage or unnecessary elaboration. It's appropriately sized for a simple data retrieval tool.

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 (data retrieval tool with 1 parameter), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the parameter, behavioral aspects, or return values. For a tool in a crowded namespace with 45 siblings, more context is needed to help an agent use it correctly.

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?

The input schema has 1 parameter ('stock_code') with 0% schema description coverage (the schema provides only a title and default value). The description doesn't mention this parameter at all, failing to compensate for the low coverage. It doesn't explain what 'stock_code' represents, what format it should be in, or how it affects the data retrieval.

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机构持仓数据' translates to 'Get F10 institutional holding data', which provides a clear verb ('get') and resource ('F10 institutional holding data'). However, it doesn't distinguish this tool from its many siblings (like 'holders', 'top_holders', 'capital_history', etc.), which likely also deal with holding/ownership data. The purpose is understandable 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. With 45 sibling tools including several related to holdings (e.g., 'holders', 'top_holders'), there's no indication of what makes this tool unique or when it should be preferred over others. No context about prerequisites or exclusions is mentioned.

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