Skip to main content
Glama
liqiongyu

Xueqiu MCP

by liqiongyu

rebalancing_history

Retrieve historical portfolio rebalancing transactions for Xueqiu investment portfolios to analyze trading patterns and strategy adjustments.

Instructions

获取组合历史交易信息

Args:
    cube_symbol: 组合代码

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cube_symbolNoSZ000002

Implementation Reference

  • main.py:287-287 (registration)
    The @mcp.tool() decorator registers the rebalancing_history function as an MCP tool.
    @mcp.tool()
  • main.py:288-295 (handler)
    The handler function for the rebalancing_history tool. It fetches the rebalancing history data from the pysnowball library (ball.rebalancing_history) for the given cube_symbol and processes the timestamps before returning the result.
    def rebalancing_history(cube_symbol: str="SZ000002") -> dict:
        """获取组合历史交易信息
        
        Args:
            cube_symbol: 组合代码
        """
        result = ball.rebalancing_history(cube_symbol)
        return process_data(result)
  • The function signature defines the input schema (cube_symbol: str with default 'SZ000002') and output type (dict). Used by FastMCP for tool schema.
    def rebalancing_history(cube_symbol: str="SZ000002") -> dict:
  • main.py:34-61 (helper)
    The process_data helper function is called by the tool handler to convert timestamps in the response data 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)
    The convert_timestamps helper recursively converts timestamp fields in the data to formatted datetime strings, used 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the tool retrieves information, implying it's read-only, but doesn't cover aspects like rate limits, authentication needs, error conditions, or response format. For a tool with no annotations, this is a significant gap in transparency.

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 brief and front-loaded with the main purpose, followed by parameter details. It avoids unnecessary verbosity, though the structure could be improved by integrating the parameter explanation more seamlessly rather than as a separate 'Args' section.

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 tool's complexity (historical data retrieval), lack of annotations, no output schema, and minimal parameter documentation, the description is incomplete. It doesn't explain what data is returned, time ranges, or how to interpret results, making it inadequate for effective agent use without additional context.

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 adds meaningful context for the single parameter 'cube_symbol' by labeling it as '组合代码' (portfolio code), which clarifies its purpose beyond the schema's generic 'Cube Symbol' title. With 0% schema description coverage and only one parameter, this compensation is adequate, though it could specify format or examples.

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 '获取组合历史交易信息' (Get portfolio historical transaction information), which clearly indicates the tool retrieves historical data for a portfolio. However, it doesn't distinguish this from similar sibling tools like 'capital_history' or 'fund_nav_history', leaving ambiguity about what specific type of 'transaction information' is provided.

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 related to historical data (e.g., capital_history, fund_nav_history), the description fails to specify 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.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/liqiongyu/xueqiu_mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server