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liqiongyu

Xueqiu MCP

by liqiongyu

blocktrans

Retrieve block trade data for specific stocks to analyze large-volume transactions in the Chinese stock market.

Instructions

获取大宗交易数据

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stock_codeNoSZ000002

Implementation Reference

  • main.py:127-131 (handler)
    The main handler function for the 'blocktrans' tool. It is registered via the @mcp.tool() decorator, defines the input schema via type hints (stock_code: str with default), fetches data using the pysnowball library's ball.blocktrans method, processes it with process_data, and returns a dict.
    @mcp.tool()
    def blocktrans(stock_code: str="SZ000002") -> dict:
        """获取大宗交易数据"""
        result = ball.blocktrans(stock_code)
        return process_data(result)
  • main.py:34-61 (helper)
    Shared helper function called by the blocktrans handler (and all other tools) to post-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)
    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. It only states the action ('获取' - get) without details on permissions, rate limits, data freshness, or output format. This is inadequate for a tool that likely involves external data fetching, as it omits critical operational context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

The description is a single, efficient phrase ('获取大宗交易数据') with zero waste. It's front-loaded and appropriately sized for the basic information it conveys, though this conciseness contributes to gaps in other dimensions.

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 low schema description coverage (0%), the description is incomplete. It fails to address key aspects like return format, error handling, or data scope, making it insufficient for reliable agent use.

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?

The description doesn't mention parameters, but with only one parameter ('stock_code') and 0% schema description coverage, the baseline is high. Since no parameters are explained in the description, it doesn't add value beyond the schema, but the minimal parameter count keeps the score from dropping lower.

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 '获取大宗交易数据' (Get block transaction data) states the action ('获取' - get) and resource ('大宗交易数据' - block transaction data), providing a basic purpose. However, it's vague about scope (e.g., historical vs. real-time, specific markets) and doesn't distinguish from siblings like 'capital_flow' or 'capital_history', which might overlap in financial data contexts.

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?

No guidance is provided on when to use this tool versus alternatives. With many sibling tools for financial data (e.g., 'capital_flow', 'kline', 'report'), the description lacks context on use cases, prerequisites, or exclusions, leaving the agent to guess based on the tool name alone.

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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