Antrieb MCP server

Antrieb MCP Server

Instant disposable clusters for LLM-generated networks and infra.

Say an LLM writes a VyOS NAT config, an OpenWrt firewall rule set, or a bash script to harden a CentOS cluster. Where do you run it before it touches your environment? Antrieb gives the LLM a disposable multi-node cluster, with real VMs and real networking, to try what it generates. Break it, quickly reprovision, try again. No cleanup.

Same OS and appliances your LLM-generated code will target in production: CentOS Stream, Ubuntu, Debian, Alma, Alpine, VyOS, OPNsense, SONiC, OpenWrt. Multi-network topologies with per-NIC assignment. Not a container, not a microVM, not some unknown Linux — the same kernels and packages your real fleet runs.

Root access, private networking, passwordless SSH between nodes. Ten minutes per cluster. Clean slate every time. Instant clusters that change the economics of being wrong for LLMs.

Quick Start

Antrieb is a remote MCP server. Nothing to install. Add it to your config and start provisioning.

Claude Web / ChatGPT

With web-based AI clients, add Antrieb as a custom connector.

In claude.ai: go to Settings → Connectors, click Add custom connector. Enter:

• Name: Antrieb
• URL: https://antrieb.sh/mcp

Click Add, then Connect and sign in. Back in your chat, try something like:

Create a 3-node Kubernetes cluster, with node1 running both as control-plane and worker, and node2/node3 as pure workers. Install nginx with 3 pods and pod anti-affinity.

Walkthrough video:

MCP Clients (Claude Desktop, Claude Code, Codex, custom clients)

Get a free API key at antrieb.sh/dash, then add this to your MCP client config (e.g. .mcp.json for Claude Desktop):

Claude Code

Put this JSON in the current folder in a file called .mcp.json. Claude Code will find it automatically.

{
  "mcpServers": {
    "antrieb": {
      "type": "http",
      "url": "https://antrieb.sh/mcp",
      "headers": {
        "Authorization": "Bearer ant_YOUR_API_KEY"
      }
    }
  }
}

Codex

Put this TOML content in ./.codex/config.toml. Then set the ANTRIEB_TOKEN environment variable with your API key before launching Codex.

[mcp_servers.antrieb]
  url = "https://antrieb.sh/mcp"
  bearer_token_env_var = "ANTRIEB_TOKEN"

No local install, no dependencies, no Docker.

How It Works

Your LLM controls real VMs through 5 tools:

1. provision  →  Spin up a cluster (sub-second per VM, declare custom networks and NICs)
2. exec       →  Run commands on any node (cluster-scoped env vars auto-injected)
3. save       →  Snapshot a node as an image, or save a runbook
4. search     →  Discover images, runbooks, or active clusters
5. delete     →  Destroy a cluster, or decommission a saved artifact

The LLM drives the entire workflow: provision a cluster, install software command by command, verify each step, and iterate until it works. Antrieb provides the infrastructure; the LLM provides the intelligence.

Example Workflow

You: provision 3 ubuntu nodes and install nginx on all nodes

LLM: provision(cluster: ["ubuntu24.04 x3"])
→ { session_id: "abc12", nodes: ["node1", "node2", "node3"], provision_time_ms: 720 }

LLM: exec(session_id: "abc12", node: "node1", command: "apt-get update && apt-get install -y nginx")
→ { exit_code: 0, stdout: "..." }

LLM: exec(session_id: "abc12", node: "node1", command: "systemctl start nginx && curl -s localhost")
→ { exit_code: 0, stdout: "<html>Welcome to nginx...</html>" }

LLM: save(type: "image", session_id: "abc12", node: "node1", name: "my-nginx", commands: [...])
→ { ani: "antrieb:my-nginx:v1" }

LLM: delete(type: "cluster", name: "abc12")
→ { success: true }

Available Images

Base Images

Shortened. The full README is on GitHub.