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liqiongyu

Xueqiu MCP

by liqiongyu

report

Retrieve institutional rating data for specific stocks to analyze investment recommendations from financial institutions.

Instructions

获取机构评级数据

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stock_codeNoSZ000002

Implementation Reference

  • main.py:99-103 (handler)
    The handler function for the 'report' MCP tool. Decorated with @mcp.tool() for registration. Fetches institutional rating data via pysnowball.ball.report() and processes the result using process_data helper.
    @mcp.tool()
    def report(stock_code: str="SZ000002") -> dict:
        """获取机构评级数据"""
        result = ball.report(stock_code)
        return process_data(result)
  • main.py:34-61 (helper)
    Shared helper function used by the 'report' tool (and others) 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 helper utility called by process_data to recursively convert timestamp fields in the data to human-readable 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
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden for behavioral disclosure. It only states what data is retrieved without mentioning any behavioral traits: no information on permissions needed, rate limits, data freshness, error handling, or output format. This is inadequate for a tool with no annotation coverage.

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 a single, efficient sentence in Chinese, making it concise and front-loaded. However, it's overly brief given the tool's potential complexity, bordering on under-specification rather than optimal conciseness.

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 no annotations, no output schema, and low parameter schema coverage, the description is incomplete. It doesn't cover behavioral aspects, parameter meanings, or what the tool returns, leaving significant gaps for an AI agent to understand and use the tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

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

The input schema has 1 parameter with 0% description coverage, and the tool description doesn't mention parameters at all. It doesn't explain what 'stock_code' represents, its format (e.g., 'SZ000002' as default), or how it affects the rating data retrieval. This fails to compensate for the low schema coverage.

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 '获取机构评级数据' (Get institutional rating data) states a general purpose but lacks specificity. It mentions the resource (institutional rating data) but doesn't specify what kind of ratings, for what entities, or how they're retrieved. It doesn't distinguish from siblings like 'suggest_stock' or 'indicator' which might also provide rating-related data.

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 (e.g., 'suggest_stock', 'indicator', 'earningforecast'), there's no indication of whether this is for historical ratings, real-time updates, or comparative analysis. The description doesn't mention prerequisites, exclusions, or specific contexts for application.

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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