You may have heard of the pillars of observability: metrics, logs, traces, and, depending on who you ask, profiles. As systems grow in complexity, the need to both individually understand and correlate these signals becomes paramount for rapid incident detection, root cause analysis, and performance optimization. Yet, even with advances like OpenTelemetry, making sense of your own data often requires learning specialized query languages and navigating complex toolchains, which is a barrier for many users.
While AI tools like ChatGPT can offer general advice, they lack access to your specific observability data. This is where Model Context Protocol (MCP) servers come in. MCP servers provide a standardized way for AI assistants and other tools to securely connect to your observability data, making it easier to investigate and diagnose issues faster using natural language.
In this talk, we’ll cover MCP and demonstrate how to explore your observability data using Grafana MCP, while also touching on how the same approach can work with other MCP-compatible tools or custom MCP servers.
