Loading...
Loading...
Found 8 Skills
How to use KubeSense MCP tools to query logs, traces, and metrics from Kubernetes clusters. Covers tool selection, the discovery-first workflow, and links to datasource-specific skills.
Inventory and topology of the monitored Kubernetes estate via KubeSense MCP — clusters, nodes, pods, workloads, detected issues, infra failures (OOM/CrashLoop/probe/scheduling), and recent deploys/scaling changes. Use for "what is running", "what is broken at the k8s layer", and "what changed".
Write, validate and run KubeSense SPL — the piped log query language — via validate-spl and execute-spl. Covers the storage-name field contract that inverts every other KubeSense surface, log_processed attributes, the command and aggregation vocabulary, and why an SPL query can compile and still fail.
Create KubeSense dashboards over metrics, logs, and traces — either directly with the create-dashboard MCP tool or as preset JSON the user imports — with the exact schema, the fields that hard-fail import, and the fields that silently discard your data instead of erroring. Includes validate-dashboard-json for checking a preset before you commit to it.
Run raw ClickHouse SQL against KubeSense logs and traces via execute-sql / validate-sql — the queryable column set, the $__timeFilter/$__clusters macros, @attr access, and the joins, CTEs and window functions the analyze-* tools cannot express.
Query Kubernetes, infrastructure and cloud-provider metrics from KubeSense with PromQL/MetricsQL — metric discovery, label inspection, the metric families KubeSense actually collects (kube-state-metrics, cAdvisor, node-exporter, OTel hostmetrics, DCGM GPU, JVM, and AWS/GCP/Azure/MongoDB Atlas/Confluent/Kong cloud resources), and the label conventions (clusterId, kubesense_cloud_resource_metric) needed to write a query that returns data.
Search and aggregate Kubernetes logs via KubeSense MCP — the exact log field catalog (type, instance, container, node — not level/pod_name/host), WHERE syntax, body text search, counts and percentiles, and window-based pagination.
Work with KubeSense alerts — survey what is firing, inspect a rule's breaching condition and history, and create rules either via the create-alert MCP tool or as import JSON over metrics, logs, and traces. Includes validate-alert-json for checking hand-built rule JSON, the alert engine's own field allow-lists, which differ from the query engine's, and translating Datadog monitors. Also covers multi-query formula rules, the Alert. placeholder namespace, manual resolve, and maintenance windows.