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liqiongyu

Xueqiu MCP

by liqiongyu

watch_list

Retrieve your personalized stock watchlist from Xueqiu to monitor preferred securities and track market positions through AI assistants.

Instructions

获取用户自选列表

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • main.py:258-262 (handler)
    The handler function for the 'watch_list' tool. It retrieves the user's watch list using the pysnowball library (ball.watch_list()) and processes the data to convert timestamps to readable datetime strings.
    @mcp.tool()
    def watch_list() -> dict:
        """获取用户自选列表"""
        result = ball.watch_list()
        return process_data(result)
  • main.py:258-258 (registration)
    The @mcp.tool() decorator registers the watch_list function as an MCP tool with the name 'watch_list'.
    @mcp.tool()
  • main.py:34-62 (helper)
    Helper function used by watch_list (and other tools) to process response data, primarily converting Unix timestamps to human-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:14-32 (helper)
    Supporting helper recursively converts timestamp fields in the data to formatted datetime strings, called by process_data.
    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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It implies a read operation ('获取' - get) but doesn't specify if it requires authentication, returns real-time or cached data, has rate limits, or details the response format. This is inadequate for a tool with zero annotation coverage.

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 ('获取用户自选列表') with zero wasted words. It's front-loaded and appropriately sized for its purpose, earning full marks for conciseness.

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 (a user-specific data retrieval tool), no annotations, and no output schema, the description is incomplete. It lacks details on authentication needs, return format, data scope, or how it differs from siblings, making it insufficient for effective agent use.

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 tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add param info, but that's acceptable here. Baseline is 4 for zero parameters, as it avoids unnecessary details.

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 '获取用户自选列表' (Get user watch list) states a clear verb ('获取' - get) and resource ('用户自选列表' - user watch list), but it's vague about what constitutes a 'watch list' (e.g., stocks, funds, other assets) and doesn't distinguish it from sibling tools like 'watch_stock' or 'suggest_stock', which might have overlapping purposes. It avoids tautology but lacks specificity.

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 doesn't mention prerequisites (e.g., user authentication), exclusions, or compare it to sibling tools like 'watch_stock' or 'suggest_stock', leaving the agent with no context for selection.

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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