Executes multi-step workflows with advanced templating, state tracking, and dynamic tool generation. The workflow engine transforms complex, multi-step platform operations into simple, single-command executions.
- Workflow Definition Management: Parse and validate YAML workflow definitions
- Dynamic Tool Generation: Generate
workflow_<name>execution tools for each defined workflow - Step Execution: Execute workflow steps with dependency management and error handling
- Template Processing: Advanced argument templating with variable substitution
- Execution Tracking: Persistent execution state and comprehensive history
- Cross-Tool Integration: Orchestrate core tools, external MCP tools, and services
executor.go: Workflow execution engine with step orchestrationexecution_tracker.go: Real-time execution state managementexecution_storage.go: Persistent execution history and resultsapi_adapter.go: API integration for tool access
Each workflow definition automatically generates a corresponding execution tool:
.muster/workflows/connect-monitoring.yaml → workflow_connect-monitoring
.muster/workflows/check-cilium-health.yaml → workflow_check-cilium-health
.muster/workflows/login-management-cluster.yaml → workflow_login-management-cluster
- Input Variables:
{{ .input.argName }}for workflow arguments - Step Results:
{{ .results.stepId.fieldName }}for step result chaining — every step's result is referenceable by later steps regardless of any flag - One Expression Language: step args, condition templates, and condition
jsonPathexpectations all use the same Go-template/sprig engine and the same path navigator (dotted paths with array indexing, e.g.items[0].name) - Default Values: Built-in support for argument defaults
- Type Validation: Schema validation for all arguments
outputflag: marks the steps whose results are included in the returned document (the deprecatedstoreflag is a backwards-compatible alias)outputtemplate: a workflow-level templated object rendered against.input/.results/.varsafter all steps complete, returned in place of the default response to produce a small, structured result (JSON types are preserved)
- Execution History: Full audit trail with
core_workflow_execution_list - State Tracking: Real-time execution progress monitoring
- Error Recovery: Configurable retry logic and error handling
- Resource Cleanup: Automatic cleanup of temporary resources
Workflows seamlessly orchestrate across tool types:
steps:
# External MCP tool usage
- id: "kubernetes-action"
tool: "x_kubernetes_port_forward"
# Conditional execution
- id: "health-check"
tool: "core_service_status"
args:
name: "monitoring-service"Workflows orchestrate services:
- Monitor health with
core_service_status - Manage lifecycle with start/stop/restart operations
Workflows can coordinate multiple external MCP servers:
- Infrastructure management (Kubernetes)
- Monitoring setup (Prometheus, Grafana)
- GitOps operations (Flux)
All workflow definitions persist in .muster/workflows/ enabling:
- Version control of operational procedures
- Team sharing of standardized workflows
- Consistent execution across environments
This architecture transforms complex, error-prone manual procedures into reliable, repeatable, single-command operations that AI agents can execute consistently.