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Alibaba Cloud MCP Server

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by aliyun

GetDiskUsageData

Monitor and analyze disk usage metrics for Alibaba Cloud ECS instances to optimize storage performance and resource allocation.

Instructions

获取磁盘利用率指标数据

Input Schema

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

Implementation Reference

  • Handler function for the CMS_GetDiskUsageData tool, which retrieves disk utilization metrics from Alibaba Cloud CMS using the _get_cms_metric_data helper. Includes Pydantic schema for inputs.
    @tools.append
    def CMS_GetDiskUsageData(
        InstanceIds: List[str] = Field(description='AlibabaCloud ECS instance ID List'),
        RegionId: str = Field(description='AlibabaCloud region ID', default='cn-hangzhou')
    ):
        """获取磁盘利用率指标数据"""
        return _get_cms_metric_data(RegionId, InstanceIds, 'diskusage_utilization')
  • Registration loop that adds all CMS tools (including CMS_GetDiskUsageData) to the FastMCP server instance.
    for tool in cms_tools.tools:
        mcp.tool(tool)
  • Core helper function that performs the actual CMS metric query for disk usage and other metrics, called by the handler.
    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 the Alibaba Cloud CMS client with region-specific endpoint, used by _get_cms_metric_data.
    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?

With no annotations provided, the description carries the full burden of behavioral disclosure but only states what the tool does without any additional context. It doesn't describe whether this is a read-only operation, potential side effects, authentication needs, rate limits, or what the output looks like (e.g., format, units). This leaves significant gaps in understanding how the tool behaves.

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 purpose without unnecessary words. It's appropriately sized for a simple tool, though it could be slightly more informative without losing conciseness.

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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., metrics format, time range, aggregation), which is critical for a data retrieval tool. The description should provide more context about the output to compensate for the missing structured data.

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 description adds no meaning beyond what the input schema provides. Since schema description coverage is 100%, the schema already documents both parameters (InstanceIds and RegionId) adequately. The baseline score of 3 is appropriate as the description doesn't compensate but doesn't need to given the high schema coverage.

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 '获取磁盘利用率指标数据' (Get disk utilization metric data) states a clear purpose with a verb and resource, but it's somewhat vague about what specific disk utilization metrics are retrieved. It doesn't distinguish itself from sibling tools like GetDiskTotalData or GetDiskUsedData, which likely provide related but different disk metrics.

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 any prerequisites, context, or exclusions, leaving the agent to infer usage from the tool name and parameters alone, which is insufficient for effective tool selection.

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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