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k8s-telemetry-mcp

by kubeopsai
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Python
Hybrid

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    TDQS

    A3.9/5.0

    Scored across 23 tools

    Disambiguation4/5

    Most tools have clearly distinct targets—logs, metrics, traces, cluster health, AWS Config, CloudTrail, alerts—so an agent can usually pick correctly. A few pairs overlap in raw inputs or sources (query_pod_logs vs analyze_logs, query_cloudtrail vs get_resource_history), but the descriptions clarify the different purposes.

    Naming Consistency5/5

    All tools follow a consistent operation_object snake_case pattern: query_* for raw backend queries, get_* for fetching known telemetry, and search/analyze/build/enrich/check for higher-level analysis. There is no stylistic mixing or unpredictable naming.

    Tool Count4/5

    23 tools is on the high side, but the server covers Kubernetes logs, metrics, traces, events, alerts, SLOs, cost, and AWS audit/config/inspection data, so each tool has a distinct role. It feels slightly over-packed rather than bloated, and the breadth justifies the count.

    Completeness4/5

    The surface covers the full observability workflow: raw queries, pod logs, metrics, traces, cluster health, events, deployments, alerts, SLOs, cost, and AWS audit/config/compliance. The main gap is resource discovery—there is no explicit list_pods/list_namespaces tool, so agents must already know target names or infer them from other tools.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

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