Grafana MCP Server

The Grafana MCP server bridges the gap between an AI assistant and a Grafana deployment, exposing the full breadth of Grafana’s monitoring and observability capabilities through a standardized Model Context Protocol interface. By turning Grafana dashboards, datasources, Prometheus and Loki queries, incidents, and Sift investigations into first‑class MCP tools, the server allows an AI to understand, manipulate, and generate actionable insights from real‑time telemetry data without leaving the conversational context. This eliminates the need for developers to write custom integrations or manually export JSON, enabling instant data‑driven dialogue.

At its core, the server offers a rich set of dashboard operations. Developers can search for dashboards by title or metadata, retrieve concise summaries that highlight panel counts and variable sets, and fetch or patch individual properties using JSONPath expressions. These targeted tools keep the context window lean—critical when dealing with large dashboards that would otherwise bloat the AI’s memory. The server also supports full dashboard creation and updates, though these consume more context due to the need for complete JSON payloads. The inclusion of panel query extraction provides an AI with direct access to the underlying PromQL or LogQL expressions, facilitating deeper analysis and automatic query generation.

Beyond dashboards, the server exposes Grafana’s datasource catalog, allowing an AI to list all configured Prometheus and Loki sources and retrieve detailed configuration data. This capability is essential for composing accurate queries that reference the correct datasource UID or type. The Prometheus and Loki tools support both instant and range queries, as well as metadata discovery—metric names, label values, and stream statistics—enabling the assistant to build queries on demand or suggest relevant metrics based on user intent. Incident management is also integrated: the server can search, create, and update incidents in Grafana Incident, allowing an AI to trigger alerts or document incident progress directly from a conversation.

Sift investigations are exposed through simple listing tools, giving the assistant visibility into ongoing security or operational investigations without requiring separate log queries. When combined with the query tools, an AI can correlate incidents to specific metrics or logs, providing a holistic view of system health. The server’s design prioritizes context‑window efficiency through specialized summary and property extraction endpoints, making it practical to use in large‑scale deployments where bandwidth and memory constraints are paramount.

In real‑world scenarios, a DevOps engineer might ask the AI to “show me dashboards with CPU usage panels for service X” and receive a concise summary, then request the underlying PromQL query to tweak thresholds. A security analyst could trigger an incident from a conversation and have the AI append contextual logs automatically. Because all operations are expressed as standard MCP calls, any AI assistant that understands the protocol can integrate this server seamlessly into its workflow, turning Grafana’s rich observability stack into an interactive knowledge base for rapid decision making.