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by aliyun

GetCpuLoadavgData

Monitor CPU load averages on AlibabaCloud ECS instances by retrieving one-minute load metrics, enabling performance tracking and resource optimization.

Instructions

获取CPU一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

  • The main handler function implementing the CMS_GetCpuLoadavgData tool. It fetches the 1-minute average CPU load metric data from Alibaba Cloud CMS for given ECS instance IDs and region.
    def CMS_GetCpuLoadavgData(
        InstanceIds: List[str] = Field(description='AlibabaCloud ECS instance ID List'),
        RegionId: str = Field(description='AlibabaCloud region ID', default='cn-hangzhou')
    ):
        """获取CPU一分钟平均负载指标数据"""
        return _get_cms_metric_data(RegionId, InstanceIds, 'load_1m')
  • Shared helper function used by CMS tools to query metric data from Alibaba Cloud CMS API.
    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
  • Registration of all CMS tools (including CMS_GetCpuLoadavgData) to the FastMCP server instance.
    for tool in cms_tools.tools:
        mcp.tool(tool)
  • Import of the cms_tools module containing the tool definitions and tools list.
    from alibaba_cloud_ops_mcp_server.tools import cms_tools, oos_tools, oss_tools, api_tools, common_api_tools
  • Helper function to create the 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)
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 states it 'gets' data, implying a read-only operation, but doesn't mention any behavioral traits such as authentication requirements, rate limits, error conditions, or what the returned data looks like (e.g., format, units). This leaves significant gaps for a tool interacting with a cloud service.

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 a single, efficient sentence in Chinese that directly states the tool's purpose without any unnecessary words or fluff. It is appropriately sized and front-loaded, making it easy to parse quickly.

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 of interacting with AlibabaCloud ECS instances and the lack of annotations and output schema, the description is incomplete. It doesn't cover behavioral aspects like authentication, error handling, or data format, and there's no output schema to clarify return values. This makes it inadequate for safe and effective tool invocation in a cloud environment.

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?

The schema description coverage is 100%, with clear descriptions for both parameters (InstanceIds and RegionId). The description adds no additional parameter semantics beyond what the schema provides, such as explaining the format of InstanceIds or implications of RegionId. Given the high schema coverage, the baseline score of 3 is appropriate.

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' meaning 'get') and resource ('CPU一分钟平均负载指标数据' meaning 'CPU one-minute average load metric data'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from its siblings GetCpuloadavg15mData and GetCpuloadavg5mData, which appear to be similar tools for different time intervals.

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. There are no explicit instructions about when/when-not to use it, nor does it mention sibling tools like GetCpuloadavg15mData or GetCpuloadavg5mData for different time intervals, leaving the agent to infer usage context.

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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