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AlibabaCloud MCP Server

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by aliyun

GetMemUsageData

Retrieve memory utilization metrics for AlibabaCloud ECS instances by specifying region and instance IDs, enabling precise resource monitoring and management.

Instructions

获取内存利用率指标数据

Input Schema

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

Implementation Reference

  • The handler function for the 'CMS_GetMemUsageData' tool, which fetches memory usage utilization data from Alibaba Cloud CMS for specified ECS instances.
    @tools.append
    def CMS_GetMemUsageData(
        InstanceIds: List[str] = Field(description='AlibabaCloud ECS instance ID List'),
        RegionId: str = Field(description='AlibabaCloud region ID', default='cn-hangzhou')
    ):
        """获取内存利用率指标数据"""
        return _get_cms_metric_data(RegionId, InstanceIds, 'memory_usedutilization')
  • Core helper function that queries the CMS API for metric data (used by GetMemUsageData and similar tools).
    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 a configured CMS client for the specified region.
    def create_client(region_id: str) -> cms20190101Client:
        config = create_config()
        config.endpoint = f'metrics.{region_id}.aliyuncs.com'
        return cms20190101Client(config)
  • Initialization of the tools list where CMS_GetMemUsageData is appended via decorator.
    tools = []
  • Pydantic Field definitions providing input schema for the tool.
    InstanceIds: List[str] = Field(description='AlibabaCloud ECS instance ID List'),
    RegionId: str = Field(description='AlibabaCloud region ID', default='cn-hangzhou')
Behavior2/5

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

With no annotations provided, the description carries full burden but only states what the tool does ('获取' - get) without behavioral details. It doesn't disclose whether this is a read-only operation, requires specific permissions, has rate limits, returns time-series vs snapshot data, or what format/units the data comes in. For a metric tool with zero annotation coverage, this is insufficient.

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 sentence in Chinese that directly states the tool's function without fluff. However, it could be more front-loaded with critical context (e.g., specifying it's for AlibabaCloud ECS instances, which is only in the schema).

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 metric-fetching tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'memory utilization metric data' entails (e.g., percentages, time ranges, aggregation), return format, or error conditions. Given the complexity of cloud monitoring data and lack of structured output, more context is needed.

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 both parameters (InstanceIds, RegionId) well-documented in the schema. The description adds no parameter-specific information beyond implying memory metrics are fetched, which the schema already covers through parameter names and descriptions. Baseline 3 is appropriate when schema does the heavy lifting.

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

Purpose3/5

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

The description '获取内存利用率指标数据' (Get memory utilization metric data) states a clear verb ('获取' - get) and resource ('内存利用率指标数据' - memory utilization metric data), but it's vague about scope and doesn't differentiate from siblings like GetMemUsedData. It doesn't specify whether this is historical, real-time, aggregated, or per-instance data.

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?

No guidance on when to use this tool versus alternatives like GetMemUsedData or other metric tools (GetCpuUsageData, GetDiskUsageData). The description doesn't mention prerequisites, timing considerations, or comparison with sibling tools, 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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