Dcc Mcp

Default DCC control skill — connect to and operate live Maya, Blender, Houdini, Photoshop, 3ds Max, Nuke, Unreal, Substance 3D, and other DCC apps through structured DCC-MCP tools. Use this skill first whenever the user asks to do something in a DCC app, even when they do not mention DCC-MCP. 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.

Hal@loonghao

Install

openclaw skills install @loonghao/dcc-mcp

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”, or “edit this in Photoshop” are sufficient triggers.

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:

Prefer structured DCC-MCP tools over direct application scripting, Computer Use, or generic shell automation.
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.
Search by the user's intent and target DCC, copy the returned tool slug, inspect its schema and annotations, then call it.
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.
Use scoped Computer Use 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. Pick the one that matches how the user works — do not force IDE users onto the CLI, and do not ask agents to configure MCP when they can run shell.

Dimension	Agent path (this skill)	IDE path (native MCP)
Who	OpenClaw, Hermes, Codex CLI, CI bots, custom agent runtimes, any host with shell	Cursor, Claude Desktop, VS Code MCP, other MCP-native clients
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; optional `dcc-mcp-cli` on `PATH` or `--ensure-cli` with consent	Add gateway URL to IDE MCP settings (see repo `docs/guide/*`)
When to choose	Host has no MCP connector, runs headless, or studio wants one forkable skill	User already works inside an IDE with MCP configured
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:

Use this routing policy first for every DCC-control request, whether the host is MCP-native or shell-only.
MCP-native 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.
Shell-only host — use dcc-mcp-cli (search → describe → call).
Do not mix paths in one turn — pick CLI+REST or MCP for the whole task, not both.
Zero instances — stop, explain, ask consent before bootstrap; see references/ZERO_INSTANCES_CLI.md.

Computer Use fallback

Do not choose app-ui 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 app-ui:

app_ui__snapshot with an exact process_id, window_handle, or window_title.
app_ui__find and a semantic app_ui__act when possible; otherwise one screenshot-coordinate app_ui__act using that snapshot.
app_ui__snapshot after every action before choosing the next action.
app_ui__stop_computer_use when the fallback completes, fails, or is abandoned so the visible effects and input owner are released.

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_APP_UI_UIA_PROCESS_ID or DCC_MCP_APP_UI_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 app_ui__snapshot(resume_computer_use=true) after the user explicitly asks to resume Computer Use.

For an exact PID/HWND, app_ui__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.

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.

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.

What auto-ensure does

Probe GET /health on the gateway port.
If healthy -> continue.
If unreachable -> acquire a launch lock, spawn the gateway daemon in the background, poll until healthy, then release the lock.
Run the original command; local control commands still use FileRegistry and direct per-DCC MCP after the gateway lifecycle check succeeds.

CLI usage

bash

# Local inventory - auto-ensures the loopback gateway first
dcc-mcp-cli list

# Startup diagnostics - no service launch or download
dcc-mcp-cli doctor

# Gateway health check - auto-starts loopback gateway if needed
dcc-mcp-cli health

# Disable auto-start for one command
dcc-mcp-cli --no-auto-gateway list

# Explicit daemon lifecycle
dcc-mcp-cli gateway daemon start
dcc-mcp-cli gateway daemon status
dcc-mcp-cli gateway daemon restart

# Set longer auto-start wait timeout
dcc-mcp-cli --auto-gateway-timeout-secs 30 health

gateway daemon start and gateway daemon restart are the durable operator paths: their start phase passes --gateway-idle-timeout-secs 0 by default so an explicitly managed local daemon does not exit just because no DCC backend is registered yet.

Explicit ensure result format

json

{
  "host": "127.0.0.1",
  "port": 9765,
  "already_running": true,
  "pid": 12345
}

already_running: true -> gateway was already up; proceed to endpoint-level gateway commands.
already_running: false -> gateway was just started by this call (includes pid).

If auto-ensure fails

Symptom	Likely cause	Action
Timeout after `--auto-gateway-timeout-secs`	Gateway binary missing or port conflict	Ask user to install dcc-mcp-core or check port availability
Lock contention	Concurrent launch race	Retry after a short delay
Port 0 rejected	Invalid config	Verify `DCC_MCP_GATEWAY_PORT` or `--port` is non-zero
Remote profile or `DCC_MCP_BASE_URL` unreachable	Auto-start only applies to local loopback HTTP URLs	Report the remote gateway as unreachable

Python fallback note

The Python fallback (dcc_gateway.py) does NOT include a gateway ensure command because the ensure flow spawns a subprocess daemon - a capability specific to the compiled CLI. When the CLI binary is unavailable, skip to dcc_gateway.py health directly. If health fails, report the gateway as unreachable and ask the user to start a gateway manually or install dcc-mcp-cli.

Connection Order

Use dcc-mcp-cli list for local inventory, or dcc-mcp-cli list --gateway <name> for a remote profile.
Use dcc-mcp-cli for all subsequent commands when it is on PATH.
If missing, ask user permission, then download dcc-mcp-cli from GitHub Releases.
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

dcc-mcp-cli stores remote gateway profiles under ~/.dcc-mcp/gateway-profiles.json. The Python helper still reads a gateway URL from DCC_MCP_BASE_URL.

bash

dcc-mcp-cli list
dcc-mcp-cli gateway register https://workstation.example:19293 --name pcA
dcc-mcp-cli gateway list
dcc-mcp-cli list --gateway pcA
python scripts/dcc_gateway.py --base-url http://127.0.0.1:9765 health

For a one-off command:

bash

python scripts/dcc_gateway.py --base-url http://127.0.0.1:9765 health

Quick probe helper:

bash

python3 scripts/check_cli.py
py -3 scripts\check_cli.py

Flags: --base-url URL, --cli dcc-mcp-cli, --ensure-cli, --install-dir DIR, --pretty.

When the user approves downloading the CLI:

bash

# Linux / macOS
python3 scripts/dcc_gateway.py --ensure-cli list
vx python scripts/dcc_gateway.py --ensure-cli list

# Windows
py -3 scripts\dcc_gateway.py --ensure-cli list
vx python scripts\dcc_gateway.py --ensure-cli list

Release assets are selected by platform:

Platform	Asset
Windows x86_64	`dcc-mcp-cli-windows-x86_64.exe`
Linux x86_64	`dcc-mcp-cli-linux-x86_64`
macOS Intel/Apple Silicon	`dcc-mcp-cli-macos-universal2`

If Python is not easy to locate, install vx first and run the helper through vx python:

bash

# Linux / macOS
curl -fsSL https://raw.githubusercontent.com/loonghao/vx/main/install.sh | bash

# Windows PowerShell
powershell -c "irm https://raw.githubusercontent.com/loonghao/vx/main/install.ps1 | iex"

Step 0 — Local Inventory First

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

bash

# 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 — Mandatory Instance Inventory

Run this every time the user starts/stops a DCC host:

bash

# CLI (primary)
dcc-mcp-cli list
dcc-mcp-cli health

# Python fallback (when CLI is unavailable)
python scripts/dcc_gateway.py health
python scripts/dcc_gateway.py list

Interpret dcc-mcp-cli list:

json

{
  "total": 1,
  "instances": [
    {
      "instance_id": "full-uuid",
      "instance_short": "a1b2c3d4",
      "dcc_type": "maya",
      "status": "available",
      "stale": false,
      "mcp_url": "http://127.0.0.1:8765/mcp"
    }
  ]
}

Report to the user:

total
Count by dcc_type
Any stale: true rows
The target instance_id or instance_short you will use

If total == 0, stop and ask whether the user wants setup guidance for the target DCC. Continue only after explicit 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 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:

text

maya.a1b2c3d4.maya_primitives__create_sphere

Never hand-build slugs.

Step 3 — Describe Schema

bash

# 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

bash

# CLI (primary)
dcc-mcp-cli call maya.a1b2c3d4.maya_primitives__create_sphere \
  --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 \
  --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 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:

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.

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.

bash

# 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:

bash

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

Use marketplace commands for skills:

bash

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:

bash

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:

bash

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.

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.