Skip to main content
Glama
zilliztech

Zilliz MCP Server

Official
by zilliztech

query_cluster_metrics

Retrieve performance metrics for a Zilliz Cloud cluster, including compute usage, storage, request counts, latency, and success rates over specified time periods.

Instructions

Query the metrics of a specific cluster.

Args:
    cluster_id: ID of the target cluster
    start: Starting date and time in ISO 8601 timestamp format (optional, use with end)
    end: Ending date and time in ISO 8601 timestamp format (optional, use with start)
    period: Duration in ISO 8601 duration format (optional, use when start/end not set)
    granularity: Time interval for metrics reporting in ISO 8601 duration format (minimum PT30S)
    metric_queries: List of metric queries, each containing 'metricName' and 'stat' fields
        - metricName: Name of the metric. Available options:
            * CU_COMPUTATION - Compute unit computation usage
            * CU_CAPACITY - Compute unit capacity
            * STORAGE_USE - Storage usage
            * REQ_INSERT_COUNT - Insert request count
            * REQ_BULK_INSERT_COUNT - Bulk insert request count
            * REQ_UPSERT_COUNT - Upsert request count
            * REQ_DELETE_COUNT - Delete request count
            * REQ_SEARCH_COUNT - Search request count
            * REQ_QUERY_COUNT - Query request count
            * VECTOR_REQ_INSERT_COUNT - Vector insert request count
            * VECTOR_REQ_UPSERT_COUNT - Vector upsert request count
            * VECTOR_REQ_SEARCH_COUNT - Vector search request count
            * REQ_INSERT_LATENCY_P99 - Insert request latency P99
            * REQ_BULK_INSERT_LATENCY_P99 - Bulk insert request latency P99
            * REQ_UPSERT_LATENCY_P99 - Upsert request latency P99
            * REQ_DELETE_LATENCY_P99 - Delete request latency P99
            * REQ_SEARCH_LATENCY_P99 - Search request latency P99
            * REQ_QUERY_LATENCY_P99 - Query request latency P99
            * REQ_SUCCESS_RATE - Request success rate
            * REQ_FAIL_RATE - Request failure rate
            * REQ_FAIL_RATE_INSERT - Insert request failure rate
            * REQ_FAIL_RATE_BULK_INSERT - Bulk insert request failure rate
            * REQ_FAIL_RATE_UPSERT - Upsert request failure rate
            * REQ_FAIL_RATE_DELETE - Delete request failure rate
            * REQ_FAIL_RATE_SEARCH - Search request failure rate
            * REQ_FAIL_RATE_QUERY - Query request failure rate
            * ENTITIES_LOADED - Number of loaded entities
            * ENTITIES_INSERT_RATE - Entity insert rate
            * COLLECTIONS_COUNT - Collection count
            * ENTITIES_COUNT - Total entity count
        - stat: Statistical method (AVG for average, P99 for 99th percentile - P99 only valid for latency metrics)
Returns:
    Dict containing cluster metrics data
    Example:
    {
        "code": 0,
        "data": {
            "results": [
                {
                    "name": "CU_COMPUTATION",
                    "stat": "AVG", 
                    "unit": "percent",
                    "values": [
                        {
                            "timestamp": "2024-06-30T16:09:53Z",
                            "value": "1.00"
                        }
                    ]
                }
            ]
        }
    }
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cluster_idYes
startNo
endNo
periodNo
granularityNoPT30S
metric_queriesNo
Behavior3/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It partially succeeds by describing the return format with an example and noting parameter constraints (e.g., 'minimum PT30S' for granularity). However, it lacks important behavioral context such as authentication requirements, rate limits, error conditions, or whether this is a read-only operation (though 'query' implies read).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately structured with clear sections (Args, Returns, Example) but is quite lengthy due to the exhaustive metric list. While this detail is valuable for parameter semantics, it makes the description less concise. The core purpose is front-loaded, but the extensive parameter documentation dominates the text.

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

Completeness4/5

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

For a 6-parameter tool with no annotations and no output schema, the description provides strong coverage. It documents all parameters thoroughly, includes a return example, and explains key constraints. The main gaps are the lack of behavioral context (auth, rate limits) and usage guidance relative to sibling tools, but given the parameter complexity, it's reasonably complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Given 0% schema description coverage, the description compensates exceptionally well by providing comprehensive parameter documentation. It explains each parameter's purpose, format requirements (ISO 8601), optionality, usage rules (e.g., 'use with end'), constraints (minimum granularity), and for 'metric_queries', it provides a complete list of available metric names with descriptions and valid 'stat' values. This adds substantial meaning beyond the bare schema.

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 tool's purpose: 'Query the metrics of a specific cluster.' This is a specific verb ('query') and resource ('metrics of a specific cluster'), though it doesn't explicitly differentiate from sibling tools like 'describe_cluster' or 'list_clusters' which might provide different types of cluster information.

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. While it mentions optional parameters and their usage patterns (e.g., 'use with end'), it doesn't explain when to query metrics versus using other cluster-related tools like 'describe_cluster' or 'list_clusters' for different information needs.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/zilliztech/zilliz-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server