Connect Grafana to AI with Improvado MCP

When something goes wrong, Grafana fires dozens of correlated alerts simultaneously. The on-call engineer receives a wall of notifications, all pointing at symptoms rather than root cause. Identifying which single failure is causing the cascade requires manually opening 8 dashboards and correlating timelines.

Your AI agent correlates all firing alerts in a single query, identifies the earliest anomaly in the causal chain, and proposes the root cause — presenting a prioritized triage summary instead of a list of symptoms.

Engineering teams create dashboards organically over time. A mature Grafana instance has hundreds of dashboards, many with overlapping metrics, inconsistent naming, and abandoned panels. Finding the one dashboard that shows the specific metric you need — and trusting it's current — can take longer than the investigation itself.

Ask your AI agent to search across all Grafana dashboards for panels matching a metric name, service, or label. It surfaces the relevant panel with its current value and data source, regardless of which dashboard it lives in or how it was named.

After an incident, the post-mortem requires reconstructing exactly what happened and when — which metric spiked, which alert fired, which deploy preceded the anomaly. This means scrubbing through multiple dashboards with time range selectors, exporting data points, and stitching together a narrative manually.

Your AI agent pulls metric data, alert history, and deployment annotations from across Grafana into a single chronological timeline. Ask for an incident reconstruction and receive a ready-to-paste narrative with timestamps and metric values.