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liqiongyu

Xueqiu MCP

by liqiongyu

fund_nav_history

Retrieve historical net asset value data for investment funds to analyze performance trends over time.

Instructions

获取基金历史净值数据

Args:
    fund_code: 基金代码

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fund_codeNoSZ000002

Implementation Reference

  • main.py:431-439 (handler)
    The handler function implementing the 'fund_nav_history' MCP tool. Decorated with @mcp.tool() for automatic registration. Fetches historical NAV data via the pysnowball library (imported as 'ball') and processes the response using the shared process_data helper function.
    @mcp.tool()
    def fund_nav_history(fund_code: str="SZ000002") -> dict:
        """获取基金历史净值数据
        
        Args:
            fund_code: 基金代码
        """
        result = ball.fund_nav_history(fund_code)
        return process_data(result)
  • main.py:34-61 (helper)
    Helper function used by the fund_nav_history tool (and others) to post-process raw data from pysnowball, primarily converting timestamps to readable dates.
    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:431-431 (registration)
    The @mcp.tool() decorator registers the fund_nav_history function as an MCP tool.
    @mcp.tool()
Behavior2/5

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

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It implies a read operation but doesn't disclose rate limits, authentication needs, data freshness, or response 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.

Conciseness3/5

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

The description is brief but front-loaded with the purpose. The Args section is redundant with the schema and could be omitted for better conciseness. It's efficient but not optimally structured.

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?

For a tool with no annotations, no output schema, and minimal parameter documentation, the description is incomplete. It lacks details on return values, error handling, and behavioral traits, 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 adds meaningful context for the single parameter 'fund_code' by explaining it's a fund code, which compensates for the 0% schema description coverage. However, it doesn't specify format constraints (e.g., 'SZ000002' example in schema) or optionality, keeping it from a perfect score.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as '获取基金历史净值数据' (Get fund historical NAV data), which is a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'nav_daily' or 'fund_detail' that might provide related data, preventing a perfect score.

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 doesn't mention prerequisites, context, or comparisons to sibling tools like 'nav_daily' or 'fund_detail', leaving the agent without usage direction.

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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