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liqiongyu

Xueqiu MCP

by liqiongyu

suggest_stock

Search for Chinese stock symbols using keywords to identify relevant companies on the Xueqiu platform.

Instructions

关键词搜索股票代码

Args:
    keyword: 关键词

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordNoSZ000002

Implementation Reference

  • main.py:497-505 (handler)
    The handler function for the 'suggest_stock' tool. It is decorated with @mcp.tool() for registration and executes the core logic by calling the underlying pysnowball library's suggest_stock method with the provided keyword, then processes the result using process_data.
    @mcp.tool()
    def suggest_stock(keyword: str="SZ000002") -> dict:
        """关键词搜索股票代码
        
        Args:
            keyword: 关键词
        """
        result = ball.suggest_stock(keyword)
        return process_data(result)
  • main.py:34-61 (helper)
    Helper function used by suggest_stock (and other tools) 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 recursively called by process_data to 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
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 mentions '搜索' (search) but doesn't specify if this is a read-only operation, what the output format might be (e.g., list of suggestions with codes/names), or any limitations like rate limits. The description is too minimal to adequately inform an agent about behavioral traits.

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 concise with two sentences and an Args section, avoiding unnecessary verbosity. It's front-loaded with the main purpose, though the structure could be improved by integrating the parameter info more seamlessly rather than as a separate note.

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 (a search tool with no annotations, 0% schema coverage, and no output schema), the description is incomplete. It doesn't explain what the tool returns (e.g., a list of matching stocks), how results are ordered, or any error conditions. For a tool that likely outputs structured data, this lack of detail hinders effective use by an agent.

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 description includes an 'Args' section that lists 'keyword: 关键词', adding minimal semantics beyond the schema. However, with 0% schema description coverage and no output schema, the description doesn't compensate enough—it doesn't explain what constitutes a valid keyword (e.g., partial names, codes, symbols) or how results are structured. This leaves 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 the tool's purpose as '关键词搜索股票代码' (keyword search for stock codes), which provides a basic verb+resource combination. However, it doesn't differentiate from sibling tools like 'quotec' or 'quote_detail' that might also retrieve stock information. The purpose is clear but lacks specificity about what makes this tool unique.

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. With many sibling tools related to stocks (e.g., 'quotec', 'quote_detail', 'kline'), there's no indication of whether this is for fuzzy matching, exact codes, or initial discovery. The user must infer usage from the 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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