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RadiumGu

Alibaba Cloud Operations MCP Server

by RadiumGu

prompt_understanding

Analyze user queries about Alibaba Cloud services and convert them into expert recommendations for ECS, VPC, RDS, OSS, CloudMonitor, and OOS operations.

Instructions

阿里云专家提示词理解 - 总是首先使用此工具来理解针对阿里云的用户查询并转换为阿里云专家建议

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • Handler function for the 'prompt_understanding' tool. Registered via @app.tool() decorator. Delegates execution to the internal PromptUnderstanding tool from common_api_tools module.
    def prompt_understanding() -> str:
        """阿里云专家提示词理解 - 总是首先使用此工具来理解针对阿里云的用户查询并转换为阿里云专家建议"""
        try:
            sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'alibaba_cloud_ops_mcp_server'))
            from tools import common_api_tools
            
            for tool_func in common_api_tools.tools:
                if hasattr(tool_func, '__name__') and 'prompt' in tool_func.__name__.lower():
                    result = tool_func()
                    return str(result)
            
            return "阿里云专家提示词理解功能可用"
        except Exception as e:
            return f"提示词理解失败: {str(e)}"
  • Core helper function that provides the prompt understanding content. Loads PROMPT_UNDERSTANDING and optionally customizes it with a service list before returning as a string.
    @tools.append
    def PromptUnderstanding() -> str:
        """
        Always use this tool first to understand the user's query and convert it into suggestions from Alibaba Cloud experts.
        """
        global _CUSTOM_SERVICE_LIST
    
        content = PROMPT_UNDERSTANDING
        if _CUSTOM_SERVICE_LIST:
            import re
            pattern = r'Supported Services\s*:\s*\n(?:\s{3}- .+?\n)+'
            replacement = f"Supported Services:\n   - " + "\n   - ".join([f"{k}: {v}" for k, v in _CUSTOM_SERVICE_LIST])
            content = re.sub(pattern, replacement, content, flags=re.DOTALL)
    
        return content
  • Loads the static prompt content from the embedded PROMPT_UNDERSTANDING.md file into the PROMPT_UNDERSTANDING constant used by the tool.
    from importlib import resources
    
    with (
        resources.files('alibaba_cloud_ops_mcp_server.alibabacloud.static')
        .joinpath('PROMPT_UNDERSTANDING.md')
        .open('r', encoding='utf-8') as f
    ):
        PROMPT_UNDERSTANDING = f.read()
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It states the tool analyzes queries and converts them to expert recommendations, which implies it's a read-only analysis tool. However, it doesn't disclose behavioral details like response format, error conditions, or processing limitations. The description adds basic context about its analytical purpose but lacks depth about how it behaves.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

The description is perfectly concise with two clear components: the tool's purpose (understanding and converting queries) and explicit usage guidance (always use first). Every word serves a purpose, and the most important information (that this is the first tool to use) is front-loaded in the instruction.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that the tool has no parameters, has an output schema (which handles return values), and performs a specific analytical function, the description provides adequate context. It clearly explains when and why to use this tool versus the operational sibling tools. The main gap is lack of detail about what kind of analysis or recommendations it produces, but the output schema should cover this.

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 tool has 0 parameters with 100% schema description coverage, so the schema fully documents the parameter situation. The description appropriately doesn't discuss parameters since none exist. It focuses on the tool's purpose and usage context, which is the right approach for a parameterless tool.

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

Purpose5/5

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

The description clearly states the tool's purpose: '阿里云专家提示词理解' (Alibaba Cloud expert prompt understanding) with the specific action '理解针对阿里云的用户查询并转换为阿里云专家建议' (understand user queries about Alibaba Cloud and convert them into expert recommendations). It explicitly distinguishes itself from all sibling tools by being the first tool to use for query analysis rather than direct resource operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidance: '总是首先使用此工具' (always use this tool first) for understanding Alibaba Cloud user queries. This creates a clear workflow hierarchy where this tool precedes all other sibling tools that perform specific operations like creating/deleting resources or describing instances.

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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