DCC-MCP

DCC control and marketplace Skill discovery

DCC-MCP — Default DCC Control

Route DCC intent here first. MCP-native agents call the structured gateway tools directly; shell-only agents use dcc-mcp-cli — no MCP connector required.

Use this skill whenever the user asks to operate a supported DCC application. In an MCP-native host, use the gateway's structured inventory, search, describe, load, and call tools. In an agent or headless CLI host without an MCP connector, control DCC-MCP through dcc-mcp-cli. The CLI uses local FileRegistry + direct per-DCC MCP in the built-in local profile, and gateway REST (/v1/search, /v1/describe, /v1/call) for named remote profiles.

Local direct calls are excluded from Gateway stats. For evidence or Skill reflection, add --require-gateway --agent-session-id <task-id> from the first call; this route fails closed without direct fallback.

The compatibility default remains JSON for scripts. Agents should pass --output toon to reduce the command result's context-token cost; use JSON only when another program must parse it. The bundled Python fallback is gateway-REST only and sends Accept: application/json because it must parse the response internally.

CLI Invocation Contract

Run documented commands directly; do not preflight them with dcc-mcp-cli <command> --help. Follow CLI-returned next_step.command and next_step.arguments unchanged. Use targeted subcommand help at most once per CLI version only after the documented syntax is rejected or when an option is not covered here. Do not request --output json for agent-readable output.

bash

dcc-mcp-cli reload-skills --instance-id <instance-id> --output toon
dcc-mcp-cli load-skill <skill-name> --instance-id <instance-id> --output toon
dcc-mcp-cli stop-instance --dcc-type <dcc-type> --instance-id <instance-id> --output toon

stop-instance is only for a test-owned instance that advertises a safe-stop hook. Its --dcc-type and --instance-id flags are both required.

Marketplace Intent — Search Unless the Exact ID Is Known

Requests to find, compare, or recommend a DCC-MCP marketplace Skill must start with the official CLI catalog, even when the user says “Skill store”, “marketplace”, or “商城” without naming DCC-MCP. Install/update requests without an exact package ID follow the same discovery path:

bash

dcc-mcp-cli marketplace search --query "maya rigging" --limit 20
dcc-mcp-cli marketplace inspect <exact-name-from-search>

Marketplace discovery does not require a live DCC instance. Do not apply the live-inventory total == 0 stop rule to marketplace search or inspect. Use the user's capability words first. If there are no results, retry once with a shorter capability query or without the DCC filter; never invent a package name or substitute a web recommendation for the CLI result.

Installing or updating changes local state. Inspect unfamiliar packages and obtain user consent before marketplace install or update. When the exact ID is already known, install it directly with --reload; then use load-skill only when needed. The Python REST fallback does not implement marketplace commands, so a missing CLI follows the consent-gated official CLI installation path below. Each CLI invocation performs a short read-only marketplace update check; report updates and ask confirmation before marketplace update. uninstall --reload removes and refreshes; omit --dcc only for a package installed on one DCC.

DCC Intent Routing — Use This Skill First

Treat a request as a DCC-MCP task when the user asks to create, edit, inspect, simulate, animate, render, composite, export, or automate content in a DCC application. The user does not need to say “DCC-MCP”, “MCP”, “gateway”, or a tool name. Natural requests such as “in Maya…”, “help me in Blender…”, “render this in Houdini”, “edit this in Photoshop”, “operate Unreal”, or “control the Blender window” are sufficient triggers.

Treat “operate/control <DCC>” as a stable trigger for this skill. If the requested object is a menu, dialog, window, button, text field, pointer, or keyboard interaction, select the DCC UI Control fallback after inventory and structured-tool discovery. Do not confuse this product capability with a host agent's generic Computer Use feature.

User intent	Target inventory filter	Typical capability search
Model, rig, animate, shade, or render in Maya	maya	the requested modeling, rigging, animation, material, or render operation
Build or modify a Blender scene	blender	the requested scene, mesh, material, animation, or render operation
Create procedural geometry, FX, USD, or Karma output in Houdini	houdini	the requested SOP, DOP, Solaris, material, animation, or render operation
Edit, retouch, mask, or export an image in Photoshop	photoshop	the requested document, layer, selection, filter, or export operation
Work in 3ds Max, Nuke, Unreal, Substance 3D, or another supported host	that host's dcc_type	the user's task in plain language

For these requests:

1. Prefer structured DCC-MCP tools over direct application scripting, DCC UI Control, generic Computer Use, or shell automation.
2. If host support is unclear, run dcc-mcp-cli dcc-types; use its exact dcc_type value instead of guessing aliases.
3. Inventory live instances before choosing a host. If more than one matching instance exists, use task context or ask the user which scene/session owns the change.
4. Search once by the user's intent and target DCC, then follow the returned next_step. Describe only when requested; otherwise call directly or pass correlated load arguments unchanged.
5. Use raw scripting only when no typed tool covers the operation and the adapter exposes an explicit, policy-compliant automation tool. A repeated scripting pattern is a candidate for a reusable DCC skill.
6. Use scoped DCC UI Control only after structured tools report the operation as unsupported or the required host control is not exposed.

If the requested DCC is installed but no live adapter instance is registered, follow the zero-instance flow. Do not silently switch to GUI automation or a different DCC application.

Agent Path vs IDE Path

DCC-MCP supports two integration paths. dcc-mcp-cli is the default for every shell-capable agent. Native MCP remains the fallback for MCP-only IDE clients or when the user explicitly chooses that integration.

Dimension	Agent path (this skill)	IDE path (native MCP)
Who	OpenClaw, Hermes, Codex CLI, CI bots, custom agent runtimes, and any other host with shell access	MCP-only Cursor, Claude Desktop, VS Code MCP, or another client without shell access
Transport	dcc-mcp-cli → local MCP or remote gateway REST	MCP Streamable HTTP → gateway /mcp
Discovery surface	search → returned next_step via CLI or bundled Python helper	Gateway MCP tools: search, describe, load_skill, call
Setup	Install this skill and keep the official dcc-mcp-cli on PATH; installation/download requires user consent	Add gateway URL to IDE MCP settings (see repo docs/guide/*)
When to choose	Default whenever the agent can run shell commands	The client cannot run shell commands or the user explicitly requests native MCP
Resources / prompts	Not covered here; use REST /v1/context or IDE MCP if needed	resources/read, prompts/get, SSE subscribe via MCP

Decision rules for agents loading this skill:

1. Use this routing policy first for every DCC-control request, whether the host is MCP-native or shell-only.
2. Shell-capable host — use dcc-mcp-cli (inventory → one narrow search → returned next_step), even when a native MCP connector is also available.
3. MCP-only host — call the gateway/DCC structured tools directly (inventory → one narrow search → returned next_step). Do not ask the user to switch clients or manually repeat the operation.
4. Do not mix paths in one turn — pick CLI+REST or MCP for the whole task, not both.
5. Zero instances — stop, explain, ask consent before bootstrap; see references/ZERO_INSTANCES_CLI.md.

CLI/MCP preflight and installation

Run dcc-mcp-cli list first. If the process launches, the CLI is installed; list ensures the local gateway and enumerates DCC/MCP instances. A health or inventory error means the CLI exists: run dcc-mcp-cli doctor. Do not reinstall it, probe import dcc_mcp_core, or read server internals to infer availability. Only a shell-level command-not-found result means the CLI is missing. Ask for user consent, then immediately run python scripts/check_cli.py --ensure-cli --pretty from the loaded dcc-mcp Skill directory. The same approval covers this one verified install attempt; the helper installs the official release, rechecks health/inventory, and fails closed on manifest, URL, or SHA-256 errors. See the CLI cheatsheet.

DCC UI Control fallback

Load the DCC UI Control runtime with dcc-mcp-cli load-skill ui-control only when structured DCC capabilities cannot reach the required semantic UI:

1. ui_control__snapshot with an exact process_id, window_handle, or window_title.
2. ui_control__find and one semantic ui_control__act when possible.
3. ui_control__snapshot after every action before choosing the next action.
4. ui_control__stop_computer_use when the fallback completes, fails, or is abandoned.

The UI Control session_id identifies its scoped UI session, not stats attribution. Use --agent-session-id <task-id> for _meta.agent_context.session_id.

For reusable demonstrations, start with the gateway call's --agent-session-id, use structured tools first, then stop and review. After inspecting the redacted timeline, compile --reviewed creates a local Skill and WorkflowSpec. replay requires a new --approve-replay grant and current tool/schema. Recorded approvals, instance/control ids, coordinates, credentials, and secrets grant no authority. Never skip search, describe, or post-step verification.

Do not switch UI/input paths after policy, authorization, authentication, security, confirmation, desktop_unavailable, or user_interrupted results; the user or environment must resolve them first. Never widen scope, reuse stale coordinates, or resume without an explicit request. Load the runtime Skill for the complete target-binding, system-operation, capture, and artifact contract.

Internal studios can fork this skill once and reuse the same CLI+REST workflow across agents without maintaining per-host MCP server lists.

Gateway Profiles And Local-First Inventory

dcc-mcp-cli has a built-in local profile. In local mode, agent-control commands first ensure the machine-wide loopback gateway is healthy, then list reads the core default FileRegistry directly, and search, describe, load-skill, call, wait-ready, and guarded stop-instance talk to the selected local DCC instance's advertised MCP/readyz/safe-stop endpoints. Remote machines are selected through named gateway profiles:

Treat list as inventory plus diagnostics, not proof that a row is callable. It intentionally keeps live booting / dispatch_status=unavailable sidecar rows visible. Local search, describe, load-skill, call, and reload-skills route only to rows ready for local CLI control. Per-DCC sidecar rows become local MCP routes once they report dispatch_status=ready; before that, they remain visible for diagnostics. Use wait-ready or doctor when a listed instance is still booting.

bash

dcc-mcp-cli gateway register https://workstation.example:19293 --name pcA
dcc-mcp-cli gateway list
dcc-mcp-cli gateway set pcA
dcc-mcp-cli gateway set local
dcc-mcp-cli list --gateway pcA

Use --gateway <name> to override the current profile for one command. --base-url / DCC_MCP_BASE_URL remain direct endpoint overrides for legacy scripts and smoke checks.

Use --require-gateway for any local workflow whose calls must appear in Gateway audit/stats. Pair it with --agent-session-id <task-id> so every single or batched call gets the same _meta.agent_context.session_id without hand-editing --meta-json. A conflicting session value in --meta-json is an error. Direct local call output reports control_route=local_mcp_direct and gateway_stats_recorded=false; gateway-routed output reports control_route=gateway and gateway_stats_recorded=true.

Agent-control commands (list, search, describe, load-skill, call, wait-ready, reload-skills, and stop-instance) and endpoint-level commands such as health, update, and smoke without an explicit --url auto-ensure loopback HTTP gateway targets. File-only commands and explicit lifecycle commands do not auto-start the gateway. When startup state is unclear, run dcc-mcp-cli doctor before troubleshooting adapters. It reports profile config/current selection, the registry directory and local inventory, direct-control readiness counts, gateway daemon status, and server binary path/source/version without launching or downloading anything. When list shows local rows, prefer direct_control.recommended_next_action over guessing from status text; sidecar rows are local tool-call routes only after direct_control.ready=true. If direct_control.ready=false, inspect direct_control.diagnostics.failure_stage, failure_reason, host_rpc_*, and any diagnostics.logs.* paths before retrying. doctor summarizes the same not-ready rows under local.inventory.direct_control.not_ready_instances.

Detailed daemon lifecycle, profile commands, release assets, and fallback behavior live in CLI cheatsheet. Read it only when setup, lifecycle, or transport troubleshooting is needed.

Install This Agent Skill

Use this package to operate an existing DCC. For a new adapter use dcc-mcp-creator; for a DCC-specific Skill use dcc-mcp-skills-creator.

bash

openclaw skills install @loonghao/dcc-mcp
npx --yes clawhub@0.23.1 install @loonghao/dcc-mcp

The published package is @loonghao/dcc-mcp. Install it with the command for the current agent host, start a new agent turn, and invoke $dcc-mcp explicitly if automatic routing is uncertain. A checkout may load this directory directly.

Then follow the CLI/MCP preflight above.

dcc-mcp supersedes dcc-cli-gateway; do not load both names in one agent.

Critical Rules

Situation	You MUST
Marketplace/Skill store intent	Search the official catalog before recommendations or when no exact package ID was supplied; an exact known ID may go directly to consent-gated marketplace install --reload; live inventory is not required
Starting any local DCC task	Run dcc-mcp-cli list; it ensures the local gateway, then reads the local FileRegistry
Startup state is ambiguous	Run dcc-mcp-cli doctor; inspect selected profile, registry dir, local inventory, direct-control readiness counts, daemon status, and server binary diagnostics
Starting any remote DCC task	Select or override a profile with dcc-mcp-cli gateway set <name> or dcc-mcp-cli list --gateway <name>
Task needs gateway stats or Skill reflection	Add --require-gateway --agent-session-id <task-id> before the first tool call and keep the same task ID for all calls; do not mix direct and measured routes
Shell reports dcc-mcp-cli command-not-found	Ask permission, then run python scripts/check_cli.py --ensure-cli --pretty; the approved helper installs and rechecks health/inventory without another confirmation
CLI runs but gateway auto-ensure fails	Run dcc-mcp-cli doctor; do not reinstall the CLI or inspect Python-package/server internals
Inventory returns total == 0	Stop; do not run search, describe, or call
Remote gateway unreachable	Stop; explain; ask user permission before troubleshooting
User has not agreed to setup	Do not install packages, edit env files, launch GUI apps, or write configs
User approved setup	Follow references/ZERO_INSTANCES_CLI.md
Timeout, temporary unreachable, or DCC restart	Preserve operation IDs and follow the recovery contract in references/CLI_CHEATSHEET.md; never blindly replay a mutation or reuse stale slugs

Step 0 — Local Inventory First

Run this first when local work begins or a DCC adapter restarts:

bash

dcc-mcp-cli list
# Only when startup or readiness is unclear:
dcc-mcp-cli doctor

Interpret the result:

• list.total > 0 -> inspect status/dispatch metadata. Local search, describe, load-skill, call, and reload-skills only route to rows ready for local CLI control; use wait-ready or doctor for live-but-booting rows, including sidecars that have not reached dispatch_status=ready.
• doctor.profile.selected.mode / doctor.local.registry_dir -> confirms which local/remote mode and registry path the CLI is using before adapter setup.
• Error / timeout -> stop; explain the failure to the user. For remote profiles, the CLI cannot auto-start the gateway.

Step 1 — Select a Live Instance

Run dcc-mcp-cli list whenever a DCC starts or stops. Report total, counts by dcc_type, stale rows, and the chosen instance. If total == 0, stop and ask whether the user wants setup guidance; continue only after approval.

Step 2 — Search Tools

Only run this when inventory shows at least one non-stale target:

bash

# CLI (primary)
dcc-mcp-cli search --query "create sphere" --dcc-type maya --limit 20

# Python fallback
python scripts/dcc_gateway.py search --query sphere --dcc-type maya --limit 20

Copy the returned slug exactly and follow that hit's next_step; do not run separate broad searches for selection, geometry, and scripting unless the first result proves they are needed. Local and gateway slugs use the same agent-facing shape:

text

maya.a1b2c3d4.maya_primitives__create_sphere

Never hand-build slugs.

Step 3 — Follow next_step

• action=call — call directly; no-schema tools receive this only when compact safety hints are already present.
• action=describe — inspect the schema and safety annotations, then call.
• action=load_skill — pass the returned arguments unchanged. If the load response includes compact_schema and next_step.action=call, call directly; otherwise describe the selected target once.

bash

# Only when next_step.action=describe
dcc-mcp-cli describe maya.a1b2c3d4.maya_primitives__create_sphere

# Python fallback
python scripts/dcc_gateway.py describe maya.a1b2c3d4.maya_primitives__create_sphere

When describe or compact_schema is returned, use those exact parameter names and safety annotations before calling.

Step 4 — Call a Tool

bash

# CLI (primary)
dcc-mcp-cli call maya.a1b2c3d4.maya_primitives__create_sphere \
  --require-gateway \
  --agent-session-id task-42 \
  --json '{"radius":2.0}'

# When the workflow reserved this instance, repeat the exact lease owner.
dcc-mcp-cli call maya.a1b2c3d4.maya_primitives__create_sphere \
  --require-gateway \
  --agent-session-id task-42 \
  --json '{"radius":2.0}' \
  --meta-json '{"lease_owner":"workflow-42"}'

# Python fallback
python scripts/dcc_gateway.py call maya.a1b2c3d4.maya_primitives__create_sphere \
  --json '{"radius":2.0}'

For asynchronous render/cook tools, add --wait; the CLI polls jobs_get_status at most once per second until terminal state and writes a 5%-step progress bar plus a 30-second stalled-job heartbeat to stderr while keeping the final result on stdout. Use --wait-timeout-secs for longer runs. The bar uses progress.current, progress.total, and progress.message; do not repeatedly scan output files when typed progress exists. Native MCP/REST clients may subscribe to /v1/jobs/{job_id}/events; otherwise keep the returned job_id and use bounded status polling. Do not create a scheduled task by default. After an explicit cross-session monitoring request, schedule only a one-shot status check for that ID and stop it at terminal state. During a host reload or gateway restart, keep the ID because status stays routable; --wait reports control_plane_reconnecting then wait_recovery and returns tracking_status=owner_exited when the DCC/sidecar owner is gone; never resubmit the render or cook.

Tool-specific fields (code, file_path, radius, and similar) belong inside the --json object. Do not pass them as top-level CLI flags unless the CLI adds an explicit first-class flag later.

If the selected instance has an active pool lease, every call must carry the same lease_owner through --meta-json. Missing owner metadata fails with instance-leased; a different owner fails with lease-owner-mismatch. Do not retry either error without the matching workflow owner or a different instance. Expired leases and instances that were never leased need no owner metadata. The hidden compatibility lease workflow requires a non-empty owner without surrounding whitespace on acquire and the same owner on release; ownerless release never clears an active lease. The owner is a visible coordination label, not an authentication secret. Lease enforcement coordinates gateway and local CLI workflows; it does not protect a DCC adapter endpoint that an untrusted client can reach directly.

For generated scripts, binary descriptors, or other payloads that may exceed a shell's command-line limit, pass the JSON object through a UTF-8 file or stdin:

bash

dcc-mcp-cli call godot_project__write_script --json-file payload.json
generate_payload | dcc-mcp-cli call godot_project__write_script --json-file -

Use --json or --json-file, never both. --json-file - keeps large payloads off the process command line, which is especially important on Windows.

See references/CLI_CHEATSHEET.md for command patterns and common errors.

Step 5 — Analyze Failures and Report Bugs

Do not guess a root cause or blindly replay a mutation. Preserve request_id, trace_id, job_id, tool slug, instance id, sanitized arguments, error code, and validation result.

bash

dcc-mcp-cli doctor
dcc-mcp-cli stats --range 24h --status failure --session-id task-42
dcc-mcp-cli search --query "report feedback" --dcc-type maya
dcc-mcp-cli describe <returned-feedback-tool-slug>
dcc-mcp-cli call <returned-feedback-tool-slug> --json \
  '{"tool_name":"maya_geometry__create_sphere","intent":"Create a sphere","attempt":"radius=2.0","blocker":"Radius was ignored","severity":"blocked"}'

Use doctor for profile, registry, daemon, binary, and readiness failures. For a tool failure, refresh describe, compare the schema/annotations with the attempt, inspect failure-only stats, and call dcc_feedback__report. Its severity is blocked, workaround_found, or suggestion; it records feedback but does not create an external issue.

For a gateway-routed failure, use the CLI-returned request_id to read /v1/debug/agent-traces/<request_id> and public-safe /v1/debug/issue-reports/<request_id>. The latter supplies a bounded summary and suggested GitHub title/body. Never publish ?mode=raw without human review; create an external issue only with user authorization.

Route schema/script/Skill defects to the owning package and dcc-mcp-skills-creator; dispatch/readiness/install/wiring defects to the adapter and dcc-mcp-creator; shared gateway/CLI/protocol defects to dcc-mcp-core. Include the smallest reproduction and safe report, not hidden reasoning.

Review Reusable Friction

bash

dcc-mcp-cli stats --range 24h --dcc-type maya --session-id task-42

Only after acceptance, inspect stats_coverage. Gateway SQLite excludes local_mcp_direct; configured_route_recorded=false cannot support reflection. Re-run through --require-gateway; zero calls means missing evidence.

Load dcc-mcp-skills-creator and request review_skill_improvement with bounded task, stats, validation, and existing-skill summaries. Stats are not root-cause proof; prefer no_change, then update_existing, and create only for a repeated stable workflow. The review never authorizes out-of-scope changes.

Updates and Marketplace Maintenance

Use the gateway release manifest for binary checks. update apply stages the CLI for its next launch; a running server must be updated in its own environment. The Admin Instances panel remains check-only because the gateway cannot prove an instance's installation root. See the CLI cheatsheet for platform manifests and server-side update details.

bash

dcc-mcp-cli update check
dcc-mcp-cli update apply

For marketplace Skills, search first when the exact package ID is not known:

bash

dcc-mcp-cli marketplace search --query "maya rigging" --limit 20
dcc-mcp-cli marketplace inspect <package_name>
dcc-mcp-cli marketplace install <package_name> --dcc maya --reload

--query "maya rigging" remains supported for scripts. Search and inspect are read-only; install/update require consent. Inspect is optional when the exact package ID is already known, and --dcc is optional for single-DCC packages. After updates or installs without --reload, run reload-skills; then use load-skill only if the adapter did not auto-load it.

Use install for adapter plans, never for marketplace Skills:

bash

dcc-mcp-cli install --dcc-type maya --version 2026

Ask before --execute, follow the returned next_steps, and do not treat package installation as live registration. If no standard DCC is found, ask for an absolute path and pass --dcc-path. If auto-install is disabled, show the returned policy prompt and hand off to the named deployment owner.

What This Skill Does Not Use

• Native MCP tools/list, tools/call, or resources/read on the agent host (IDE users should use MCP instead of this skill)
• Raw curl workflows except when debugging the gateway itself
• Direct Maya/Blender/Houdini scripting

The CLI is the default agent-facing control plane. The Python fallback uses the same gateway REST endpoints only when the CLI is unavailable after a verified install attempt fails. The gateway still serves MCP for IDE clients in parallel; choosing this skill does not replace or disable the IDE MCP path.