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liqiongyu

Xueqiu MCP

by liqiongyu

business

Retrieve core business segment data for stocks to analyze revenue sources and company focus areas using Xueqiu MCP's financial API.

Instructions

获取主营业务构成数据

Args:
    stock_code: 股票代码
    count: 返回数据数量,默认5条

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stock_codeNoSZ000002
countNo

Implementation Reference

  • main.py:193-202 (handler)
    The main handler function for the 'business' MCP tool. It is decorated with @mcp.tool() which registers it with the FastMCP server. The function proxies to pysnowball.ball.business() and applies data processing via process_data().
    @mcp.tool()
    def business(stock_code: str="SZ000002", count: int = 5) -> dict:
        """获取主营业务构成数据
        
        Args:
            stock_code: 股票代码
            count: 返回数据数量,默认5条
        """
        result = ball.business(symbol=stock_code, count=count)
        return process_data(result)
  • main.py:34-61 (helper)
    Shared helper function used by the 'business' tool (and others) to process the raw data from pysnowball, including timestamp conversion.
    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 timestamps 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
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. It mentions retrieving data but doesn't specify whether this is real-time or historical, if there are rate limits, authentication requirements, error conditions, or what the output format looks like. For a data retrieval tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 more integrated. There's no unnecessary verbosity, and the information is front-loaded with the main function.

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, no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It doesn't explain what 'main business composition data' includes, how results are structured, or any limitations. For a tool that presumably returns structured business data, more context about the output would be necessary for effective use.

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 adds basic semantics for both parameters: 'stock_code: 股票代码' (stock code) and 'count: 返回数据数量,默认5条' (return data quantity, default 5 items). This provides meaning beyond the schema's titles ('Stock Code', 'Count') and default values. However, with 0% schema description coverage, it doesn't fully compensate - it lacks details like format requirements for stock_code or constraints on count values.

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: '获取主营业务构成数据' (Get main business composition data). It specifies the verb ('获取' - get) and resource ('主营业务构成数据' - main business composition data), making the function unambiguous. However, it doesn't differentiate from sibling tools like 'income' or 'report' that might also provide financial data, preventing a perfect score.

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 many sibling tools related to financial data (e.g., 'income', 'report', 'balance'), there's no indication of what makes this tool unique or when it should be preferred over others. The only usage context is implicit from the parameters.

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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