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liqiongyu

Xueqiu MCP

by liqiongyu

balance

Retrieve balance sheet data for specific stocks to analyze financial health. Specify stock codes and parameters to access structured financial information from the Xueqiu MCP server.

Instructions

获取资产负债表数据

Args:
    stock_code: 股票代码
    is_annals: 只获取年报,默认为1
    count: 返回数据数量,默认5条

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stock_codeNoSZ000002
is_annalsNo
countNo

Implementation Reference

  • main.py:167-177 (handler)
    The handler function for the 'balance' MCP tool. It is decorated with @mcp.tool() for registration, fetches asset balance sheet data from pysnowball library, processes timestamps, and returns the data as dict.
    @mcp.tool()
    def balance(stock_code: str="SZ000002", is_annals: int = 1, count: int = 5) -> dict:
        """获取资产负债表数据
        
        Args:
            stock_code: 股票代码
            is_annals: 只获取年报,默认为1
            count: 返回数据数量,默认5条
        """
        result = ball.balance(symbol=stock_code, is_annals=is_annals, count=count)
        return process_data(result)
  • main.py:34-62 (helper)
    Shared helper function used by the balance 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 recursively called by process_data to 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. While '获取' (get) implies a read operation, the description doesn't specify data format, source, freshness, rate limits, authentication requirements, or what happens when parameters are invalid. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured with a clear purpose statement followed by parameter explanations. Each sentence earns its place, though the parameter explanations could be slightly more detailed. The bilingual presentation (Chinese purpose, English parameter names) is slightly inconsistent but doesn't significantly impact clarity.

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?

For a financial data tool with 3 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what balance sheet data fields are returned, the data format, time periods covered, or how the tool handles errors or missing data. The parameter explanations help but don't provide full operational context.

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?

With 0% schema description coverage, the description provides essential semantic context for all three parameters: 'stock_code' identifies the security, 'is_annals' controls annual report filtering (with default 1), and 'count' determines result quantity (default 5). This compensates well for the schema's lack of descriptions, though it doesn't explain parameter constraints or formats.

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 '获取资产负债表数据' (get balance sheet data), which is a specific verb+resource combination. It distinguishes itself from sibling tools like 'income' or 'cash_flow' by focusing specifically on balance sheet data. However, it doesn't explicitly differentiate from all possible financial data tools beyond the name.

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 like 'income', 'cash_flow', 'indicator', and 'main_indicator' that might provide related financial data, there's no indication of when balance sheet data specifically is needed versus other financial statements or metrics.

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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