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liqiongyu

Xueqiu MCP

by liqiongyu

index_perf_90

Retrieve 90-day performance data for stock market indices to analyze recent trends and returns.

Instructions

获取指数最近90天收益数据

Args:
    index_code: 指数代码

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
index_codeNoSZ000002

Implementation Reference

  • main.py:365-373 (handler)
    The tool handler for 'index_perf_90'. It is decorated with @mcp.tool() for registration. Calls the pysnowball library's index_perf_90 method and processes the result using process_data helper.
    @mcp.tool()
    def index_perf_90(index_code: str="SZ000002") -> dict:
        """获取指数最近90天收益数据
        
        Args:
            index_code: 指数代码
        """
        result = ball.index_perf_90(index_code)
        return process_data(result)
  • main.py:34-61 (helper)
    Helper function used by the tool to process the raw data from pysnowball, including 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 utility 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It states what data is retrieved but doesn't describe the return format, whether it's paginated, if there are rate limits, authentication requirements, or error conditions. For a data retrieval tool with zero annotation coverage, this leaves significant behavioral aspects undocumented.

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 documentation. The two-sentence structure is efficient with minimal waste, though the parameter documentation could be more informative given the lack of schema descriptions.

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 data retrieval tool with no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It doesn't explain what format the performance data returns, what metrics are included, or how the 90-day period is calculated. The context signals indicate this tool needs more comprehensive documentation to be fully usable.

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 includes an 'Args' section that documents the single parameter 'index_code' and provides a brief explanation. However, with 0% schema description coverage, the description doesn't fully compensate by explaining the parameter format, valid values, or the default value 'SZ000002' shown in the schema. It adds some value but leaves important details unspecified.

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: '获取指数最近90天收益数据' (Get index performance data for the last 90 days). It specifies the verb ('获取' - get) and resource ('指数最近90天收益数据' - index performance data for last 90 days). However, it doesn't explicitly differentiate from siblings like 'index_perf_30' or 'index_perf_7', which appear to provide similar data for different timeframes.

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 sibling tools like 'index_perf_30' or 'index_perf_7' that likely serve similar purposes with different timeframes, nor does it specify any prerequisites or contextual constraints for usage.

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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