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liqiongyu

Xueqiu MCP

by liqiongyu

kline

Retrieve historical stock price data (K-line charts) from Xueqiu for analysis, with customizable time ranges up to specified days.

Instructions

获取K线数据。第二参数可制定从现在到N天前,默认100

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stock_codeNoSZ000002
daysNo

Implementation Reference

  • main.py:85-89 (handler)
    The handler function for the 'kline' MCP tool. It is decorated with @mcp.tool() for registration, fetches K-line data using pysnowball.ball.kline, processes the data with timestamps conversion, and returns it as dict.
    @mcp.tool()
    def kline(stock_code: str="SZ000002", days: int = 100) -> dict:
        """获取K线数据。第二参数可制定从现在到N天前,默认100"""
        result = ball.kline(stock_code, days)
        return process_data(result)
  • main.py:34-61 (helper)
    Helper function used by kline (and other tools) 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 utility 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 full burden. It mentions a default parameter value but lacks critical behavioral details such as data source, rate limits, authentication needs, error handling, or what the output format looks like. This is inadequate for a data retrieval tool with no annotation support.

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 very concise with two short sentences, making it easy to parse. It front-loads the core purpose and includes parameter guidance efficiently, though it could benefit from slightly more detail without becoming verbose.

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 retrieval, no annotations, no output schema, and low schema coverage, the description is incomplete. It lacks details on output format, error conditions, data freshness, and how it differs from sibling tools, 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.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds minimal semantics: it explains that the second parameter ('days') specifies a range from now to N days ago with a default of 100. However, with 0% schema description coverage, it doesn't clarify the first parameter ('stock_code') or provide examples, leaving significant gaps in parameter understanding.

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 states '获取K线数据' (Get K-line data), which clearly indicates the tool's purpose as retrieving financial chart data. However, it doesn't specify what type of K-line data (e.g., stock, crypto) or distinguish itself from potential sibling tools that might also retrieve financial data, making it somewhat vague.

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. The description mentions a default value for the 'days' parameter but doesn't explain use cases, prerequisites, or comparisons with sibling tools like 'quotec' or 'quote_detail' that might offer related financial data.

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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