Last modified June 21, 2026

Muster architecture

Muster runs as a central aggregator, the muster serve process, that holds all the logic and exposes a single, OAuth-protected MCP endpoint. Your AI assistant connects straight to that endpoint over HTTPS.

The aggregator

muster serve is the central component, typically running on a management cluster. It hosts all business logic, manages the lifecycle of downstream MCP server processes, monitors their health, and exposes a meta-tools interface over HTTPS, using the MCP HTTP and SSE transports.

Modern AI assistants—Claude Code, Cursor, VS Code with GitHub Copilot, the developer portal chat, and other MCP-capable tools—support remote, OAuth-protected MCP servers natively. You point them straight at the aggregator’s URL and they handle the OAuth flow themselves:

{
  "mcpServers": {
    "muster": {
      "url": "https://muster.<management-cluster>.<base-domain>/mcp"
    }
  }
}

All aggregation, downstream authentication, and tool management live server-side, where they can be operated and updated centrally—there is nothing to keep running on your machine.

The optional local bridge

Some MCP clients can’t connect to a remote, OAuth-protected server directly—they only speak stdio, or lack a built-in OAuth flow. For those, muster agent is a thin local bridge: it converts stdio to HTTPS and performs OAuth on the client’s behalf. It holds no business logic, so it stays a single lightweight binary. If your assistant supports remote MCP with OAuth—most now do—you don’t need it.

Conflict-free tool names

The aggregator resolves tool-name conflicts by prefixing external tools with their server name, so get_pods from the Kubernetes server becomes x_kubernetes_get_pods and query from Prometheus becomes x_prometheus_query. Tool registries update dynamically as servers start, stop, or change—the assistant’s next discovery call reflects the new capabilities without any IDE restart.

Fleet-wide aggregation

For a customer operating several management clusters, a central Muster instance aggregates the mcp-kubernetes and mcp-prometheus servers on each management cluster, giving SREs a single MCP endpoint for the entire fleet:

The user authenticates once through their enterprise identity provider. Muster bridges that identity to each remote cluster. It reuses the same token where the issuer is trusted, or exchanges it for one valid on the remote cluster where the issuer differs. The Security page covers this token handling in detail.

Two deployment shapes are supported:

• Single management cluster: Muster with mcp-kubernetes and mcp-prometheus on one management cluster. The simplest setup.
• Multiple management clusters: a central Muster that bridges SSO to the mcp-kubernetes and mcp-prometheus servers on remote management clusters. Required when a customer runs more than one management cluster.

Workflows cut agent token cost

Muster can package a multi-step operation—authenticate, port-forward, query, correlate—as a single named workflow that an agent invokes with one call. It’s not just convenient. It makes the AI assistant dramatically cheaper, because one workflow call replaces the whole discover-query-correlate loop the agent would otherwise run itself.

One internal lab trial measured this directly on four real management-cluster alerts, with the same agent, model, and prompt, differing only in whether the agent was given the raw aggregated tools or the matching workflow tool. The numbers below are illustrative of the shape of the saving rather than a guarantee—the ratios hold across a range of investigations, but the absolute figures depend on the model and its pricing:

Cache-read input tokens dominate the bill, so the 10x reduction there is the real cost lever, and it holds regardless of the per-token price. The savings scale with how much investigation an alert needs.

The trade-off is scope: a workflow only checks what it was authored to check. For a first responder handling the specific alert that paged, that focus is a feature. For open-ended “what’s broken here?” exploration, an agent with the raw tools surfaces more adjacent context at higher cost. Both modes run through the same gateway—see meta-tools for how an agent reaches each one.