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Alibaba Cloud MCP Server

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by aliyun

GetCpuloadavg15mData

Retrieve the 15-minute CPU load average data for Alibaba Cloud ECS instances to monitor performance and optimize resource utilization. Specify the region and instance IDs for targeted analysis.

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 that retrieves the 15-minute average CPU load data for specified ECS instances using Alibaba Cloud CMS metrics API.
    @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')
  • Loop that registers all tools from cms_tools (including CMS_GetCpuloadavg15mData) with the FastMCP server instance.
    for tool in cms_tools.tools:
        mcp.tool(tool)
  • Core helper function that queries the Alibaba Cloud CMS API for the last metric value (used by CPU load tools including 15m).
    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
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. The description only states what data is retrieved without mentioning authentication requirements, rate limits, error conditions, or the format/structure of the returned data. For a data retrieval tool with zero annotation coverage, this leaves significant behavioral gaps.

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. It's appropriately front-loaded with the core action and resource, making it easy to parse quickly.

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

Completeness3/5

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

Given the tool's moderate complexity (retrieving specific metric data), lack of annotations, and absence of an output schema, the description is minimally adequate but incomplete. It identifies what data is fetched but doesn't cover behavioral aspects like authentication, error handling, or data format, which are important for a cloud service tool interacting with AlibabaCloud ECS instances.

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?

Schema description coverage is 100%, so the input schema already fully documents both parameters (InstanceIds and RegionId). The description adds no additional parameter semantics beyond what's in the schema, such as explaining what the CPU load metric represents or how the fifteen-minute average is calculated. The baseline score of 3 reflects adequate coverage when the schema does the heavy lifting.

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 verb ('获取' meaning 'get') and resource ('CPU十五分钟平均负载指标数据' meaning 'CPU fifteen-minute average load metric data'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like GetCpuloadavg5mData or GetCpuLoadavgData, which appear to provide similar CPU load metrics 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 sibling tools like GetCpuloadavg5mData, GetCpuLoadavgData, and GetCpuUsageData available, there's no indication of what distinguishes this specific fifteen-minute average metric from other CPU-related metrics or when it would be preferred.

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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