| name | workflow |
| description | Infrastructure skill — multi-step action orchestration: run a sequence of MCP tools in order, passing results between steps. Use when chaining two or more tools into a repeatable pipeline (select → rename → validate → export). Not for single-tool operations or DCC-specific business logic — use a domain skill for those. |
| license | MIT |
| metadata | {"dcc-mcp":{"dcc":"python","version":"1.0.0","layer":"infrastructure","search-hint":"chain, sequence, pipeline, multi-step, orchestration, workflow, batch, run steps, automate","tags":"workflow, orchestration, chain, pipeline, automation, infrastructure","tools":"tools.yaml"}} |
Workflow Orchestration
Multi-step action chaining for DCC pipelines.
Overview
The workflow__run_chain tool lets an agent (or a human) execute a sequence
of dcc-mcp-core actions in order. Results from earlier steps flow into later
steps via context merging and {key} parameter interpolation.
Example: Select → Rename → Validate → Export
{
"steps": [
{
"label": "List mesh objects",
"action": "maya_scene__list_objects",
"params": {"type": "mesh"}
},
{
"label": "Rename with prefix",
"action": "maya_scene__rename_objects",
"params": {"prefix": "char_"}
},
{
"label": "Validate naming",
"action": "maya_pipeline__validate_naming",
"params": {}
},
{
"label": "Export FBX",
"action": "maya_scene__export_fbx",
"params": {"output": "/tmp/export.fbx"},
"stop_on_failure": true
}
]
}
Error Recovery
If a step fails and stop_on_failure is true, the chain halts immediately
and returns the partial results so far, plus the error details. The agent can
then use dcc_diagnostics__screenshot or dcc_diagnostics__audit_log to
investigate before retrying.
Context Interpolation
Use {key} placeholders in params to inject values from the running context:
{"action": "export_fbx", "params": {"output": "{export_path}"}}
The context starts from the context input, then accumulates each step's
context output.