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liqiongyu

Xueqiu MCP

by liqiongyu

main_indicator

Retrieve key financial indicators (F10 data) for Chinese stocks by entering a stock code. This tool provides essential metrics for investment analysis through the Xueqiu MCP server.

Instructions

获取F10主要指标数据

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stock_codeNoSZ000002

Implementation Reference

  • main.py:217-222 (handler)
    The handler function decorated with @mcp.tool(), defining the input schema via type hints and docstring, implementing the tool logic by calling the pysnowball library's main_indicator method and processing the result with process_data.
    @mcp.tool()
    def main_indicator(stock_code: str="SZ000002") -> dict:
        """获取F10主要指标数据"""
        result = ball.main_indicator(stock_code)
        return process_data(result)
  • main.py:34-61 (helper)
    Supporting helper function used by the main_indicator tool (and all others) to process the raw data from pysnowball, 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)
    Recursive helper utility 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. '获取' (get) implies a read operation, but there's no information about rate limits, authentication requirements, data freshness, error conditions, or what format the data returns. For a financial data tool with zero annotation coverage, this represents a significant behavioral transparency 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 extremely concise - a single Chinese phrase. While this is efficient, it may be too brief given the tool's likely complexity. There's no wasted language, but it may be under-specified rather than appropriately concise. The structure is simple and front-loaded with the core action.

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 no annotations, no output schema, and 0% parameter documentation, the description is inadequate. It doesn't explain what 'F10 main indicator data' includes, how it differs from other indicator tools, what the return format is, or any behavioral characteristics. Given the context of financial data tools where precision matters, this description leaves too many questions unanswered.

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 mentions no parameters at all, while the schema shows one parameter (stock_code). With 0% schema description coverage, the description doesn't compensate by explaining what stock_code represents, what format it should be in, or what the default 'SZ000002' signifies. The baseline is 3 since there's only one parameter, but the description adds no value beyond what's minimally visible in the schema.

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主要指标数据' (Get F10 main indicator data) states a clear verb ('获取' - get) and resource ('F10主要指标数据' - F10 main indicator data). However, it doesn't distinguish this tool from sibling tools like 'indicator' or 'report' which likely provide related financial data. The purpose is understandable but lacks specificity about what makes this 'main indicator' data unique.

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 providing financial data (balance, cash_flow, income, indicator, etc.), there's no indication of what distinguishes 'main indicator' data or when it should be preferred over other data sources. The description offers no context about use cases or prerequisites.

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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