In German, Muster means "pattern" or "sample." This project provides the building blocks for AI agents to discover patterns and collect samples from any digital environment. It gives them a universal protocol to interact with the world.
Muster is a universal control plane built on the Model Context Protocol (MCP) that solves the MCP server management problem for platform engineers and AI agents.
As a platform engineer, you interact with countless services: Kubernetes, Prometheus, Grafana, Flux, ArgoCD, cloud providers, and custom tooling. While tools like Terraform and Kubernetes operators provide unified orchestration interfaces, debugging and monitoring still requires jumping between different tools and contexts.
The MCP Revolution: LLM agents (in VSCode, Cursor, etc.) + MCP servers should solve this by giving agents direct access to your tools. There are already many excellent MCP servers available (Kubernetes, Prometheus, Grafana, Flux, etc.).
But there's a problem:
- Adding all MCP servers to your agent pollutes the context and increases costs
- Turning servers on/off manually is tedious and error-prone
- Tool discovery becomes overwhelming as your toolkit grows
- No coordination between different MCP servers and their prerequisites
Muster solves this by creating a meta-MCP server that manages all your MCP servers and provides your agent with intelligent tool discovery capabilities.
📖 Learn More: MCP Aggregation Deep Dive | System Architecture
muster servestarts the control plane that manages your MCP server processes- Configure
muster agentas an MCP server in your IDE - Your agent gets meta-tools like
list_tools,filter_tools,call_tool - Agent discovers and uses tools dynamically based on the current task
graph TD
subgraph "Your IDE (VSCode/Cursor)"
Agent["🤖 AI Agent"]
IDE["IDE MCP Config"]
end
subgraph "Muster Control Plane"
MusterAgent["🎯 muster agent<br/>(Meta-MCP Server)"]
MusterServe["⚙️ muster serve<br/>(Process Manager)"]
subgraph "Managed MCP Servers"
K8s["🔷 Kubernetes<br/>(kubectl, helm)"]
Prom["📊 Prometheus<br/>(metrics, alerts)"]
Grafana["📈 Grafana<br/>(dashboards)"]
Flux["🔄 Flux<br/>(GitOps)"]
end
end
Agent <-->|"MCP Protocol"| MusterAgent
MusterAgent <--> MusterServe
MusterServe <--> K8s
MusterServe <--> Prom
MusterServe <--> Grafana
MusterServe <--> Flux
📖 Learn More: Component Interaction Diagram | System Overview
Your agent can now:
# Discover available tools dynamically
agent: "What Kubernetes tools are available?"
→ filter tools {pattern="kubernetes"}
# Find the right tool for the task
agent: "I need to check pod logs"
→ filter tools {description="logs"}
# Execute tools on-demand
agent: "Show me failing pods in default namespace"
→ call x_kubernetes_list {"resourceType": "pods", "namespace": "default"}
📖 Learn More: MCP Tools Reference | Tool Discovery Guide
- Lifecycle Control: Start, stop, restart MCP servers on demand
- Health Monitoring: Automatic health checks and recovery
- Configuration Management: Hot-reload server configurations
- Local Process Deployment: Local processes (
localCommand) for MCP server execution
📖 Learn More: MCP Server Management | Configuration Guide
- Tool Filtering: Block destructive tools by default (override with
--yolo) - Project-Based Control: Different tool sets for different projects
- Context Optimization: Only load tools when needed
📖 Learn More: Security Configuration
Once your agent discovers how to complete a task, persist it as a workflow:
name: debug-failing-pods
steps:
- id: find-pods
tool: x_kubernetes_get_pods
args:
namespace: "{{ .namespace }}"
status: "failed"
- id: get-logs
tool: x_kubernetes_get_logs
args:
pod: "{{ steps.find-pods.podName }}"
lines: 100
Benefits:
- Reduce AI costs (deterministic execution)
- Faster results (no re-discovery)
- Consistent debugging across team members
📖 Learn More: Workflow Creation Guide | Workflow Component Architecture
Many MCP servers need setup (port-forwarding, authentication, etc.). ServiceClasses define these prerequisites:
name: prometheus-access
startTool: x_kubernetes_port_forward
args:
service: "prometheus-server"
namespace: "monitoring"
localPort: 9090
healthCheck:
url: "http://localhost:9090/api/v1/status"
Complete Integration Example:
- ServiceClass creates port-forwarding to Prometheus
- MCP Server configuration uses the forwarded port
- Workflow orchestrates: setup → query → cleanup
- Agent executes everything seamlessly
📖 Learn More: ServiceClass Patterns | Service Configuration | Services Component Guide
Connect Muster to your IDE for smart tool access:
Set up Muster for infrastructure management:
Configure your development environment:
git clone https://github.com/giantswarm/muster.git
cd muster && go build .
📖 Learn More: Installation Guide | Local Demo
Create kubernetes-server.yaml:
apiVersion: muster.io/v1
kind: MCPServer
name: kubernetes
spec:
type: localCommand
command: ["mcp-kubernetes"]
autoStart: true
Register it:
./muster create mcpserver kubernetes.yaml
Configure your IDE to use Muster's agent as an MCP server:
Cursor/VSCode settings.json:
{
"mcpServers": {
"muster": {
"command": "muster",
"args": ["standalone"]
}
}
}
📖 Learn More: AI Agent Integration | Cursor Advanced Setup
Your agent now has meta-capabilities:
list_tools: Show all available toolsfilter_tools: Find tools by name/descriptiondescribe_tool: Get detailed tool informationcall_tool: Execute any tool dynamically
📖 Learn More: Complete MCP Tools Reference | CLI Command Reference
ServiceClass for cluster access
name: cluster-login
version: "1.0.0"
serviceConfig:
serviceType: "auth"
args:
cluster:
type: "string"
required: true
lifecycleTools:
start: { tool: "x_teleport_kube_login" }
Workflow to compare pods on two clusters
# Workflow for cross-cluster investigation
name: compare-pod-on-staging-prod
input_schema:
type: "object"
properties:
namespace: { type: "string" }
pod: { type: "string" }
required: ["namespace", "pod"]
steps:
- id: staging-context
tool: core_service_create
args:
serviceClassName: "cluster-login"
name: "staging-context"
params:
cluster: "staging"
- id: prod-context
tool: core_service_create
args:
serviceClassName: "cluster-login"
name: "staging-context"
params:
cluster: "production"
- id: wait-for-step
- id: compare-resources
tool: workflow_compare_pods_on_clusters
args:
# Prometheus access with port-forwarding
name: prometheus-tunnel
startTool: k8s_port_forward
args:
service: "prometheus-server"
localPort: 9090
---
# Grafana dashboard access
name: grafana-tunnel
startTool: k8s_port_forward
args:
service: "grafana"
localPort: 3000
---
# Complete monitoring workflow
name: investigation-setup
steps:
- id: setup-prometheus
serviceClass: prometheus-tunnel
- id: setup-grafana
serviceClass: grafana-tunnel
- id: configure-prometheus-mcp
tool: core_mcpserver_create
args:
name: "prometheus"
type: "localCommand"
command: ["mcp-server-prometheus"]
env:
PROMETHEUS_URL: "http://localhost:9090"
📖 Learn More: Advanced Scenarios | Configuration Examples
- Reduced AI token usage: Tools loaded only when needed
- Deterministic workflows: No re-discovery costs
- Efficient context: Smart tool filtering
- GitOps workflows: Share debugging patterns via Git
- Consistent tooling: Same tool access across team members
- Knowledge preservation: Workflows capture tribal knowledge
- Faster incident response: Pre-built investigation workflows
- Reduced context switching: All tools through one interface
- Automated prerequisites: ServiceClasses handle setup complexity
📖 Learn More: Core Benefits | Design Principles
- Quick Start Guide - Get up and running in minutes
- AI Agent Setup - IDE integration guide
- Platform Setup - Infrastructure setup
- Local Demo - Try Muster locally
- Workflow Creation - Build automation workflows
- ServiceClass Patterns - Manage service dependencies
- MCP Server Management - Configure external tools
- Troubleshooting - Common issues and solutions
- AI Troubleshooting - AI-specific debugging
- CLI Commands - Complete command reference
- Configuration - Configuration schemas
- API Reference - REST and MCP APIs
- MCP Tools - Available tools catalog
- CRDs - Kubernetes Custom Resources
- System Architecture - How Muster works
- Component Overview - Individual components
- MCP Aggregation - Core aggregation logic
- Design Decisions - Architecture decisions
- Problem Statement - Why Muster exists
- Installation - Production deployment
- Security Configuration - Security best practices
- Development Setup - Dev environment
- Testing Framework - Testing guidelines
- Code Guidelines - Development standards
- Contributing Guide: How to contribute to Muster
- Issue Tracker: Bug reports and feature requests
- Discussions: Community Q&A and use cases
Muster is a Giant Swarm project, built to empower platform engineers and AI agents with intelligent infrastructure control.