Gcx on Nicole van der Hoeven

When to use Grafana Assistant vs MCP vs GCX: Grafana GCX

Fri, 21 Aug 2026 09:00:00 +0100

Three AI tools people confuse: Assistant, MCP, and GCX. Last time I talked about the easy hand — the MCP server. Now the power hand: GCX, a CLI built for AI agents.

If Assistant is the brain and Grafana MCP is the easy hand, then GCX is the power hand — a CLI built for an agent to drive. (Quick refresher: a CLI is a command line interface — no clicking, just typed commands.)

So when does GCX win?

When you’re using terminal coding agents — Claude Code, Cursor, Codex. It writes code, pulls the exact log or metric, and verifies against it — without leaving the editor.
When you need everything. GCX wraps the entire Grafana Cloud API — SLOs, Synthetics, IRM, k6, Fleet — the esoteric corners MCP skips. The full banana.
When you want automation and CI/CD. Dry-run mode, JSON output, and exit codes an agent can act on — built for GitOps.

Do you need Grafana Assistant for this? No — not even a subscription. Bring your own brain, same as MCP: metrics, logs, dashboards, and alerts run on nothing but a service account token. You only really pay for Assistant if you call gcx assistant directly — which taps its reasoning, billed separately.

So there’s your map: Assistant’s the brain, MCP’s the easy hand, GCX is the power hand. It’s not one versus another — they work really well together to give you the best choice in how to access your Grafana stack.

Transcript

I’m differentiating Assistant, Grafana MCP and GCX and in this one I’m talking about GCX. If Assistant is the brain and Grafana MCP is the easy hand, then GCX is the power hand — a CLI built for AI agents.

Quick refresher: a CLI is a command line interface — no clicking, just typed commands. GCX is a CLI built for an agent to drive.

So when does GCX win?

When you’re using terminal coding agents — Claude Code, Cursor, Codex. It writes code, pulls the exact log or metric, and verifies against it — without leaving the editor.
When you need everything. GCX wraps the entire Grafana Cloud API — SLOs, Synthetics, IRM, k6, Fleet — the esoteric corners MCP skips — the full banana.
When you want to do automation and CI/CD. Dry-run mode, JSON output, exit codes an agent can act on — built for GitOps.

Do you need Grafana Assistant for this? No — not even a subscription. Bring your own brain, same as MCP: metrics, logs, dashboards, alerts run on nothing but a service account token. You only really pay for Assistant if you call gcx assistant directly — which taps its reasoning, billed separately.

So there’s your map: Assistant’s the brain, MCP’s the easy hand, GCX is the power hand. It’s not one versus another. They actually work really well together to give you the best choice in how to access your Grafana stack.

Resources

AI Week Day 2: Building (Grafana AI Week — Tuesday)

Tue, 28 Jul 2026 09:00:00 +0100

Day two of AI Week is all about building — the AI tools that help you write software with confidence. Two big announcements went generally available: gcx (the Grafana CLI) and the Grafana MCP server. Both connect your coding agents — Claude Code, Cursor, and friends — to your Grafana telemetry. The MCP server gives agents opinionated, ready-made tools for the common cases (querying metrics and logs, reading and updating dashboards); gcx is the more flexible, less opinionated option, built for agents from the ground up with read-only guardrails and its own bundle of agent skills. Both work for OSS and Grafana Cloud. The point: when an agent writes your code, it shouldn’t be guessing — it should be looking at how your system actually behaves in production. That’s telemetry-driven development.

Transcript

It’s day two of Grafana Labs’ AI Week, and Tuesday is all about building — the AI tools that help you build software with confidence.

Today we have two big announcements: gcx and the Grafana MCP server are both now generally available.

Both of them connect your coding agents — think Claude Code or Cursor — to your Grafana telemetry. So what’s the difference?

The Grafana MCP server gives your agent opinionated, ready-made tools for the most common use cases: querying your metrics and logs, reading and updating dashboards, that kind of thing. You can run it yourself, or use the hosted MCP server with your Grafana Cloud stack — so yes, it works for both OSS and Grafana Cloud.

gcx is the Grafana CLI, and it’s the more flexible, less opinionated option. Agents can explore it and build their own workflows, and it was designed for agents from the ground up, with read-only guardrails so your agent can’t touch anything you didn’t approve. It works with Cloud stacks and self-managed instances, and it even ships with its own bundle of agent skills.

Why does this matter? Because when an agent writes your code, it shouldn’t be guessing — it should be looking at how your system actually behaves in production. That’s telemetry-driven development.

Head to grafana.ai for everything we’re shipping this week, and I’ll see you tomorrow for day three.

CLI and MCP Basics for AI Agents (The Context Window #04)

Wed, 03 Jun 2026 18:00:00 +0100

MCP vs CLI: which one should your AI agent actually use? In this episode of The Context Window — Grafana’s livestream series on AI in observability — I’m joined by Tiffany Jernigan and three of the engineers and PMs behind Grafana’s agent tooling: Ben Sully (who wrote the Grafana MCP server), and Ward Bekker and Dafydd Thomas from the team building gcx. We get into the two main ways an AI agent can reach the outside world — the Model Context Protocol (MCP) and good old command-line interfaces — and look at when each one makes sense, the tradeoffs around context windows, tool sprawl, token cost, and reliability. We walk through Grafana’s cloud and open-source MCP servers along with gcx, the Grafana Cloud CLI built for agents, do a live side-by-side demo of both creating an SLO, and talk about how we benchmark them against each other with o11y-bench.

Timestamps

00:00:00 — Introductions
00:02:49 — The last month in AI news
00:10:19 — What is Grafana Assistant?
00:12:30 — What does it mean for Assistant to use tools?
00:13:30 — What is MCP?
00:16:56 — What is a CLI in an AI context?
00:19:17 — What are skills?
00:24:37 — Can you use a custom MCP server with Grafana Assistant?
00:25:55 — Why team MCP?
00:28:22 — The context-window cost of MCP tool definitions
00:31:50 — Tool discoverability
00:39:25 — What is gcx, and why a CLI?
00:46:13 — Demo: creating an SLO with MCP vs gcx
00:52:58 — Benchmarking MCP vs gcx with o11y-bench
00:58:10 — How MCP and gcx relate to Grafana Assistant
01:02:35 — k6 MCP vs Grafana MCP
01:03:00 — When to use which (decision guide)
01:04:29 — Adding the OSS Grafana MCP to Assistant
01:05:41 — Turtles all the way down

Resources

MCP and CLI for agents

My Context Horizon piece: Unlearnings from building Grafana Assistant

Gcx on Nicole van der Hoeven

When to use Grafana Assistant vs MCP vs GCX: Grafana GCX

Transcript

Resources

AI Week Day 2: Building (Grafana AI Week — Tuesday)

Transcript

CLI and MCP Basics for AI Agents (The Context Window #04)

Timestamps

Resources

MCP and CLI for agents

Related reading

More from The Context Window