dcc-mcp

name	dcc-mcp
description	Default DCC control skill — connect to and operate live Maya, Blender, Houdini, Photoshop, 3ds Max, Nuke, Unreal, Godot, RenderDoc, Substance 3D, and other DCC apps through structured DCC-MCP tools. Use this skill first whenever the user asks to operate or control something in a DCC app, even when they do not mention DCC-MCP. Interface-specific intent such as clicking a menu, dismissing a dialog, or controlling a window routes to DCC UI Control after structured tools are checked. OpenClaw and other shell agents use dcc-mcp-cli inventory/search/describe/call; MCP-native IDEs use the gateway MCP surface. Not for tasks unrelated to DCC software.
license	MIT-0
allowed-tools	Bash Read
metadata	{"dcc-mcp":{"dcc":"python","layer":"infrastructure","compatibility":"Cross-platform Windows/macOS/Linux. Prefers dcc-mcp-cli on PATH; can download release asset from GitHub; local profile needs no gateway env. DCC_MCP_BASE_URL is optional for remote/legacy gateway REST fallback.","version":"0.19.62","search-hint":"dcc control operate UI control menu dialog window button click keyboard Maya Blender Houdini Photoshop 3ds Max Nuke Unreal Godot RenderDoc Substance connect create edit render automate cli gateway stats 操作 控制 界面 菜单 弹窗 窗口 按钮 点击 键盘","tags":"dcc, dcc-ui-control, ui-control, maya, blender, houdini, photoshop, nuke, unreal, godot, renderdoc, cli, gateway, clawhub, openclaw"},"openclaw":{"emoji":"🖥️","homepage":"https://github.com/dcc-mcp/dcc-mcp-core/blob/main/skills/dcc-mcp/SKILL.md"}}

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.

The CLI returns JSON by default. The bundled Python fallback is gateway-REST only and sends Accept: application/json because the gateway REST API itself now defaults to compact TOON for agent-facing routes.

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 by the user's intent and target DCC, copy the returned tool slug, inspect its schema and annotations, then call it.
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.

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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 → describe → call 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 → search → describe or load-skill → call), even when a native MCP connector is also available.
3. MCP-only host — call the gateway/DCC structured tools directly (inventory → search → describe or load_skill → call). 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 installation

If dcc-mcp-cli is missing, obtain the user's consent before installing the latest official release:

# Linux/macOS
curl -fsSL https://raw.githubusercontent.com/dcc-mcp/dcc-mcp-core/main/scripts/install-cli.sh | sh

# Windows PowerShell
powershell -ExecutionPolicy Bypass -c "irm https://raw.githubusercontent.com/dcc-mcp/dcc-mcp-core/main/scripts/install-cli.ps1 | iex"

After installation, use dcc-mcp-cli update check and dcc-mcp-cli update apply to keep the CLI current. The apply step stages the new CLI for the next launch; it does not update a running dcc-mcp-server.

DCC UI Control fallback

DCC UI Control is the public capability name and ui-control is its stable CLI command. The ui_control__* names below are canonical runtime tool identifiers; do not call the feature “Computer Use” in agent-facing text.

Do not choose UI Control first. Search, describe, and call the structured DCC skill, host API, or adapter script that owns the operation. If the operation is reported as unsupported, no suitable tool exists, or semantic UI Automation cannot reach the required control, make an agent-directed transition to DCC UI Control:

1. ui_control__snapshot with an exact process_id, window_handle, or window_title.
2. ui_control__find and a semantic ui_control__act when possible; otherwise one screenshot-coordinate ui_control__act using that snapshot.
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 so the visible effects and input owner are released.

Shell agents should use the stable CLI wrapper instead of hand-building legacy tool slugs:

dcc-mcp-cli ui-control snapshot --instance-id <id> \
  --json '{"session_id":"menu","process_id":1234}'
dcc-mcp-cli ui-control find --instance-id <id> \
  --json '{"session_id":"menu","label":"Settings","role":"menu_item"}'
dcc-mcp-cli ui-control act --instance-id <id> \
  --json '{"session_id":"menu","control_id":"settings","action":"click","snapshot_id":"<snapshot_id>"}'
dcc-mcp-cli ui-control snapshot --instance-id <id> \
  --json '{"session_id":"menu","process_id":1234}'
dcc-mcp-cli ui-control stop --instance-id <id> \
  --json '{"session_id":"menu"}'

For an operator-preapproved Windows plug-in setup, use the separate typed system-operation route. It does not use a window or snapshot. The request sends only a non-sensitive operation id; the native host resolves it from the catalog selected by DCC_MCP_UI_CONTROL_SYSTEM_GRANT_ID:

dcc-mcp-cli ui-control system-operation --instance-id <id> \
  --json '{"operation_id":"enable-remote-control"}'

The host always confirms the exact target. Never use this route for passwords, tokens, shell commands, HKLM, deletion/replacement, UAC, or security settings. If it returns elevation_required, approval_required, or system_operation_not_granted, stop and hand the operation to the user or the approved installer.

Use ui-control wait for condition-based waits. Every subcommand accepts --dcc-type, --json-file, --meta-json, and --timeout-secs with the same meaning as call. Output is compact JSON by default so agents receive ids, matches, errors, and screenshot artifact paths without the repeated MCP envelope or full UIA tree. Add --full-output only for targeted diagnostics.

Do not transition or retry through another UI/input path after a policy, authorization, authentication, security, confirmation, desktop_unavailable, or user_interrupted result. Those outcomes require the user or environment to resolve the boundary first.

Never widen the scope to the desktop or reuse coordinates across snapshots. Native pointer or keyboard fallback requires one exact process_id or window_handle already bound by the adapter/operator through DCC_MCP_UI_CONTROL_UIA_PROCESS_ID or DCC_MCP_UI_CONTROL_UIA_WINDOW_HANDLE; request scope can only narrow that trusted target. Title-only and process-name scopes are observation-only. If the user presses Ctrl+Alt+Esc and the tool returns user_interrupted, stop without retrying, changing session_id, or starting a new session. Only call ui_control__snapshot(resume_computer_use=true) after the user explicitly asks to resume DCC UI Control.

For an exact PID/HWND, ui_control__snapshot automatically uses native window capture if Windows UIA enumeration fails or times out; treat the returned tree as image-only and continue with one bounded native action. On the CLI+REST path, rich images are materialized into a bounded local artifact_path; use the host agent's local image viewer on that absolute path instead of expecting base64 JSON to render in the terminal.

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

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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.

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.

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.

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Connection Order

1. Use dcc-mcp-cli list for local inventory, or dcc-mcp-cli list --gateway <name> for a remote profile.
2. Use dcc-mcp-cli for all subsequent commands when it is on PATH.
3. If missing, ask user permission, then download dcc-mcp-cli from GitHub Releases.
4. If the download fails, use the bundled Python stdlib REST fallback.

Install via OpenClaw/ClawHub, or point your agent at this SKILL.md after cloning dcc-mcp-core/skills/dcc-mcp/.

dcc-mcp supersedes the former dcc-cli-gateway skill slug. Do not install or load both names in one agent: install dcc-mcp, verify it is discoverable, then remove the old package to avoid duplicate intent routing.

---

Critical Rules

Situation	You MUST
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>
dcc-mcp-cli missing	Ask permission before --ensure-cli; fallback Python REST is allowed if download fails
CLI auto-ensure fails	Stop; explain the result; do not run agent-control or gateway endpoint commands until the gateway is reachable
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
After DCC crash/restart	Re-run list and search; old slugs may be invalid

---

Configuration

Use the local profile unless the user selected a remote gateway. Profile, fallback, and installation commands live in the CLI cheatsheet. Do not download, install, or write configuration without user consent.

---

Step 0 — Local Inventory First

Run this as the very first step every time you begin local work or after a DCC adapter restarts:

# Supported adapter identifiers, only when support is unclear
dcc-mcp-cli dcc-types

# Local FileRegistry inventory
dcc-mcp-cli list

# No-launch startup diagnostics when state is unclear
dcc-mcp-cli doctor

# Optional gateway health check
dcc-mcp-cli health

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.
• health.status == "ok" -> gateway is up when you need gateway endpoint/admin/update workflows.
• 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_id or instance_short. If total == 0, stop and ask whether the user wants setup guidance. Continue only after explicit approval.

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Step 2 — Search Tools

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

# CLI (primary)
dcc-mcp-cli search --query 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. Local and gateway slugs use the same agent-facing shape:

maya.a1b2c3d4.maya_primitives__create_sphere

Never hand-build slugs.

---

Step 3 — Describe Schema

# CLI (primary)
dcc-mcp-cli describe maya.a1b2c3d4.maya_primitives__create_sphere

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

Read tool.inputSchema and safety annotations before calling.

---

Step 4 — Call a Tool

# CLI (primary)
dcc-mcp-cli call maya.a1b2c3d4.maya_primitives__create_sphere \
  --json '{"radius":2.0}' \
  --meta-json '{"agent_context":{"session_id":"task-42"}}'

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

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

For a tool declared execution: async (or with a positive timeout hint), a remote REST profile returns a normal JSON envelope immediately with output.status="pending" and output.job_id; do not treat HTTP 202 as a failure or retry the call, because the DCC job is already running.

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:

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.

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Step 5 — Review Reusable Friction

Only after task acceptance, query narrowly scoped gateway evidence:

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

Then load dcc-mcp-skills-creator and request its review_skill_improvement prompt. Pass only bounded task, stats, validation, and existing-skill summaries. Treat total_calls == 0 as no telemetry evidence, not success. Stats show aggregates, not root cause; prefer no_change, then update_existing, and create a skill only for a repeated, stable workflow. This review does not authorize editing or publishing outside the task scope.

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Updates and Marketplace Maintenance

Use the gateway update manifest for binary checks:

Official release builds use the platform-specific manifest from the latest GitHub release by default. Set DCC_MCP_UPDATE_MANIFEST_URL only to override that source for a studio mirror or pinned deployment.

# Check whether the local CLI has an update.
dcc-mcp-cli update check

# Check a server/instance version shown in the admin panel.
dcc-mcp-cli update check --binary dcc-mcp-server --current-version 0.18.16

# Stage a CLI binary update for the next CLI launch.
dcc-mcp-cli update apply

dcc-mcp-cli update apply only stages the CLI binary. To update a running server binary, run the server-side command in that server environment:

dcc-mcp-server update check
dcc-mcp-server update apply

The Admin Instances panel is check-only because a gateway cannot prove the selected instance's installation root. On Windows, server-side apply requires the same-version dcc-mcp-ui-control-host manifest entry and both SHA-256 digests, then stages the server and host in one installation-bound transaction.

Use marketplace commands for skills:

dcc-mcp-cli marketplace search --query rigging --dcc maya --limit 20
dcc-mcp-cli marketplace inspect <package_name>
dcc-mcp-cli marketplace install <package_name> --dcc maya
dcc-mcp-cli reload-skills --dcc-type maya
dcc-mcp-cli marketplace outdated --dcc maya
dcc-mcp-cli marketplace update <package_name> --dcc maya
dcc-mcp-cli reload-skills --dcc-type maya

Use marketplace release commands for package authors and CI:

dcc-mcp-cli marketplace pack ./my-skill --out dist/
dcc-mcp-cli marketplace publish ./my-skill \
  --catalog ./marketplace.json \
  --install-url https://github.com/<owner>/<repo>/releases/download/v0.1.0/my-skill.zip \
  --sha256 sha256:<digest>

After installing or updating skills, first run dcc-mcp-cli reload-skills --dcc-type <dcc> so running adapters re-scan the marketplace skill path. Then use dcc-mcp-cli load-skill for a live instance when the adapter has not auto-loaded the skill yet.

Use install for adapter plans, not marketplace skills:

dcc-mcp-cli install --dcc-type maya --version 2026
dcc-mcp-cli install --dcc-type maya --version 2026 --python "C:/Program Files/Autodesk/Maya2026/bin/mayapy.exe"
dcc-mcp-cli install --dcc-type maya --version 2026 --python "C:/Program Files/Autodesk/Maya2026/bin/mayapy.exe" --execute

Agents must ask before using --execute. The executor prompts for consent, rolls back completed steps if a later step fails, verifies pip packages with pip show, and verifies git/zip/path installs by checking their target path. Package install is not online registration: the DCC plugin or sidecar must start and remain alive before dcc-mcp-cli list shows an instance. Treat the install JSON next_steps array as the authoritative machine-readable follow-up sequence. If it includes read-install-instructions, read that adapter repository's raw install.md first; it owns host-specific setup. Then start/enable the host plugin, run doctor, confirm list, wait for readiness, search/call tools, and use marketplace search, inspect, install, then reload-skills for optional community skill packages.

If install_policy.auto_install_enabled is false, do not retry with --execute. Show the returned install_policy.prompt to the user and hand off to the named Pipeline TD / studio deployment path. Studios set this through DCC_MCP_INSTALL_DISABLED=1 and DCC_MCP_INSTALL_DISABLED_PROMPT.

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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 download attempt fails. The gateway still serves MCP for IDE clients in parallel; choosing this skill does not replace or disable the IDE MCP path.