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AlibabaCloud MCP Server

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by aliyun

GetCpuloadavg15mData

Retrieve the 15-minute average CPU load data for specified AlibabaCloud ECS instances to monitor performance and manage resources effectively.

Instructions

获取CPU十五分钟平均负载指标数据

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
InstanceIdsYesAlibabaCloud ECS instance ID List
RegionIdNoAlibabaCloud region IDcn-hangzhou

Implementation Reference

  • Handler function implementing the CMS_GetCpuloadavg15mData tool (containing 'GetCpuloadavg15mData'), retrieves CPU 15m load average data for ECS instances.
    @tools.append
    def CMS_GetCpuloadavg15mData(
        InstanceIds: List[str] = Field(description='AlibabaCloud ECS instance ID List'),
        RegionId: str = Field(description='AlibabaCloud region ID', default='cn-hangzhou')
    ):
        """获取CPU十五分钟平均负载指标数据"""
        return _get_cms_metric_data(RegionId, InstanceIds, 'load_15m')
  • Core helper function that fetches metric data from Alibaba Cloud CMS API, used by CMS_GetCpuloadavg15mData.
    def _get_cms_metric_data(region_id: str, instance_ids: List[str], metric_name: str):
        client = create_client(region_id)
        dimesion = []
        for instance_id in instance_ids:
            dimesion.append({
                'instanceId': instance_id
            })
        describe_metric_last_request = cms_20190101_models.DescribeMetricLastRequest(
            namespace='acs_ecs_dashboard',
            metric_name=metric_name,
            dimensions=json.dumps(dimesion),
        )
        describe_metric_last_resp = client.describe_metric_last(describe_metric_last_request)
        logger.info(f'CMS Tools response: {describe_metric_last_resp.body}')
        return describe_metric_last_resp.body.datapoints
  • Helper to create CMS client configured for the given region.
    def create_client(region_id: str) -> cms20190101Client:
        config = create_config()
        config.endpoint = f'metrics.{region_id}.aliyuncs.com'
        return cms20190101Client(config)
  • Registers all CMS tools, including CMS_GetCpuloadavg15mData, to the FastMCP server.
    for tool in cms_tools.tools:
        mcp.tool(tool)
  • Imports the cms_tools module containing the tool implementations and tools list.
    from alibaba_cloud_ops_mcp_server.tools import cms_tools, oos_tools, oss_tools, api_tools, common_api_tools
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. While '获取' (get) implies a read operation, the description doesn't mention authentication requirements, rate limits, error conditions, or what format the data returns. For a cloud monitoring tool with no annotation coverage, this leaves significant behavioral questions unanswered.

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 Chinese sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized for what it communicates, though it could be more helpful with additional context.

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 cloud monitoring tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the returned data looks like, what units are used, whether it's real-time or historical data, or how to interpret the results. The agent would need to guess about the tool's behavior and output format.

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

Parameters3/5

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

With 100% schema description coverage, the input schema already documents both parameters well. The description adds no additional parameter information beyond what's in the schema. The baseline score of 3 reflects adequate parameter documentation through the schema alone, though the description contributes nothing extra.

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 action ('获取' - get/retrieve) and resource ('CPU十五分钟平均负载指标数据' - CPU 15-minute average load metric data), making the purpose understandable. However, it doesn't differentiate from its siblings like GetCpuloadavg5mData or GetCpuLoadavgData, which appear to be similar CPU load monitoring tools with different timeframes.

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 siblings like GetCpuloadavg5mData, GetCpuLoadavgData, and GetCpuUsageData, there's no indication of what distinguishes this 15-minute average tool from other CPU monitoring tools, leaving the agent to guess based on naming 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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