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AlibabaCloud MCP Server

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by aliyun

GetCpuUsageData

Monitor and retrieve CPU usage data for AlibabaCloud ECS instances by specifying region and instance IDs, enabling efficient resource management and performance tracking.

Instructions

获取ECS实例的CPU使用率数据

Input Schema

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

Implementation Reference

  • Handler function for the GetCpuUsageData tool (named CMS_GetCpuUsageData). Fetches CPU total usage metrics from Alibaba Cloud CMS for given ECS instance IDs and region. Includes input schema via Pydantic Fields.
    @tools.append
    def CMS_GetCpuUsageData(
        InstanceIds: List[str] = Field(description='AlibabaCloud ECS instance ID List'),
        RegionId: str = Field(description='AlibabaCloud region ID', default='cn-hangzhou')
    ):
        """获取ECS实例的CPU使用率数据"""
        return _get_cms_metric_data(RegionId, InstanceIds, 'cpu_total')
  • Core helper function that performs the actual API call to retrieve metric data (e.g., CPU usage) using Alibaba Cloud CMS DescribeMetricLast 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
  • Helper function to create the CMS client configured for the metrics endpoint in the specified region.
    def create_client(region_id: str) -> cms20190101Client:
        config = create_config()
        config.endpoint = f'metrics.{region_id}.aliyuncs.com'
        return cms20190101Client(config)
  • Registration of all CMS tools (including CMS_GetCpuUsageData) to the FastMCP server instance.
    for tool in cms_tools.tools:
        mcp.tool(tool)
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 '获取' (get), implying a read-only operation, but doesn't clarify aspects like authentication requirements, rate limits, data freshness, or response format. For a tool with no annotations, 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 unnecessary words. 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 no annotations, no output schema, and a read operation with potential complexity (e.g., data aggregation, time ranges), the description is incomplete. It lacks details on return values, error handling, or operational context, making it inadequate for full agent understanding without external inference.

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%, with clear descriptions for both parameters (InstanceIds and RegionId). The description adds no additional parameter semantics beyond what the schema provides, such as format details or constraints. With 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 verb ('获取' meaning 'get') and resource ('ECS实例的CPU使用率数据' meaning 'ECS instance CPU usage data'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like GetCpuloadavgData or GetMemUsageData, which target different metrics, leaving some ambiguity about when to choose this specific CPU metric tool.

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. It doesn't mention sibling tools like GetCpuloadavgData (for load averages) or GetMemUsageData (for memory), nor does it specify prerequisites, contexts, or exclusions for usage, leaving the agent to infer based on tool names 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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