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liqiongyu

Xueqiu MCP

by liqiongyu

index_perf_30

Retrieve performance data for stock indices over the past 30 days to analyze market trends and investment returns.

Instructions

获取指数最近30天收益数据

Args:
    index_code: 指数代码

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
index_codeNoSZ000002

Implementation Reference

  • main.py:354-362 (handler)
    Handler function for the 'index_perf_30' tool. Decorated with @mcp.tool() for registration. Fetches index performance data over the last 30 days using pysnowball.ball.index_perf_30 and processes the result with process_data.
    @mcp.tool()
    def index_perf_30(index_code: str="SZ000002") -> dict:
        """获取指数最近30天收益数据
        
        Args:
            index_code: 指数代码
        """
        result = ball.index_perf_30(index_code)
        return process_data(result)
  • main.py:354-354 (registration)
    The @mcp.tool() decorator registers the index_perf_30 function as an MCP tool.
    @mcp.tool()
  • main.py:34-62 (helper)
    Helper function used by index_perf_30 (and other tools) to process the raw data, 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)
    Supporting helper recursively converts timestamps in the data to readable 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 full burden for behavioral disclosure. It only states what data is retrieved ('最近30天收益数据') but doesn't describe the return format, data structure, potential rate limits, authentication requirements, or error conditions. For a data retrieval tool with zero annotation coverage, this leaves significant behavioral gaps.

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 two sentences: a clear purpose statement followed by parameter documentation. It's front-loaded with the main functionality. However, the Args section formatting could be cleaner, and there's some redundancy between the Chinese and English parameter documentation.

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 minimal schema documentation, the description is incomplete. It doesn't explain what '收益数据' (performance data) includes (returns, percentages, dates?), the data format, or how results are structured. The agent would have insufficient context to understand what this tool actually returns.

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 in Chinese. However, with 0% schema description coverage, the schema provides minimal context (just a default value and title). The description adds basic semantic meaning ('指数代码' - index code) but doesn't explain format requirements, valid values, or provide examples beyond the default.

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30天收益数据' (Get index's recent 30-day performance data). It specifies the verb ('获取' - get) and resource ('指数收益数据' - index performance data) with a time scope (30 days). However, it doesn't explicitly differentiate from sibling tools like 'index_perf_7' or 'index_perf_90' beyond the implied time frame difference.

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_7' (7-day performance) or 'index_perf_90' (90-day performance), nor does it explain why one would choose 30-day data over other time frames. There's no context about prerequisites or when-not-to-use scenarios.

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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