DCC-MCP Core

Name: DCC-MCP Core
Author: dcc-mcp

by dcc-mcpversion 0.20.51i★ 73iRuns on your computer

Registry nameio.github.dcc-mcp/dcc-mcp-core

How to install

What it does

Agent control plane for DCC and engine hosts: one MCP endpoint, one tool contract.

Connects Runs on your computer
Account or key No key needed
Category Other

Install

Pick the AI app you use, then copy the settings.

Passed 3/3 checks · Runs on your computer · ✓ Commands checked Nov 14, 2026

First: which AI do you use?

ClaudeWeb · app · phoneChatGPTWebClaude CodeTerminalCursorCode editorVS CodeCode editorOtherOther apps

Pick your app and the steps appear below.

Works with Claude, with some setup first

Install Python (uv) on your computer first (one time) · Only the Claude desktop app (Windows or Mac), not the web or phone

Install Python (uv)
One time only; it works for every server that needs Python (uv)
Open the Python (uv) download page ↗
Done
Open the Claude desktop app’s settings file
SettingsDeveloperEdit Config
Done

Paste these settings and save the file

{
  "mcpServers": {
    "dcc-core": {
      "command": "uvx",
      "args": [
        "dcc-mcp-server"
      ]
    }
  }
}

Done

Quit Claude and open it again
Done
Try it
Use DCC-MCP Core and tell me what you can do for me, then show me one thing
✓ If Claude says it is using DCC-MCP Core or shows a DCC-MCP Core tool in the answer, you are connected

1 optional setting(s) (works without them)

The command above works without these. Add them only if you want to change something, following the maker’s description.

DCC_MCP_GATEWAY_PORT Port the machine-wide gateway binds. One agent endpoint fronts every live DCC backend, so the port in the transport URL has to match this value. · default 9765

Descriptions are as written by the maker.

Menu names checked against Anthropic’s help page on Nov 11, 2026 · your screen may differ slightly by app version

ChatGPT cannot connect DCC-MCP Core yet

DCC-MCP Core runs on your computer, but ChatGPT can only connect servers that run on the web

What you can do instead

A · Use an app that can connect DCC-MCP Core

Steps for Claude →Install Python (uv) on your computer first (one time) · Only the Claude desktop app (Windows or Mac), not the web or phone
Steps for Claude Code →Install Python (uv) on your computer first (one time)
Steps for Cursor →Install Python (uv) on your computer first (one time)
Steps for VS Code →Install Python (uv) on your computer first (one time)

B · Others in Other that work with ChatGPT

Picked from the most used in the same category, only ones that connect by address with no key

Hostinger✓ ChatGPT
World Monitor✓ ChatGPT
Svelte MCP✓ ChatGPT

Works with Claude Code, with some setup first

Install Python (uv) on your computer first (one time)

Install Python (uv)
One time only; it works for every server that needs Python (uv)
Open the Python (uv) download page ↗
Done
Open Terminal
Mac: press Command+Space and type Terminal · Windows: open PowerShell
Done

Paste this command and press Enter

claude mcp add --scope user dcc-core -- uvx dcc-mcp-server

Done

Type /mcp in Claude Code
You should see DCC-MCP Core listed as connected
Done
Try it
Use DCC-MCP Core and tell me what you can do for me, then show me one thing
✓ If Claude Code says it is using DCC-MCP Core or shows a DCC-MCP Core tool in the answer, you are connected

1 optional setting(s) (works without them)

The command above works without these. Add them only if you want to change something, following the maker’s description.

DCC_MCP_GATEWAY_PORT Port the machine-wide gateway binds. One agent endpoint fronts every live DCC backend, so the port in the transport URL has to match this value. · default 9765

Descriptions are as written by the maker.

Menu names checked against Anthropic’s help page on Nov 11, 2026 · your screen may differ slightly by app version

Works with Cursor, with some setup first

Install Python (uv) on your computer first (one time)

Install Python (uv)
One time only; it works for every server that needs Python (uv)
Open the Python (uv) download page ↗
Done
Click this button to open Cursor
Open in Cursor
Cursor asks you to confirm; click Install
Done
Open the AI chat in Cursor in Agent mode
Done
Try it
Use DCC-MCP Core and tell me what you can do for me, then show me one thing
✓ If Cursor says it is using DCC-MCP Core or shows a DCC-MCP Core tool in the answer, you are connected

1 optional setting(s) (works without them)

The command above works without these. Add them only if you want to change something, following the maker’s description.

DCC_MCP_GATEWAY_PORT Port the machine-wide gateway binds. One agent endpoint fronts every live DCC backend, so the port in the transport URL has to match this value. · default 9765

Descriptions are as written by the maker.

Menu names checked against Cursor’s help page on Nov 11, 2026 · your screen may differ slightly by app version

Works with VS Code, with some setup first

Install Python (uv) on your computer first (one time)

Install Python (uv)
One time only; it works for every server that needs Python (uv)
Open the Python (uv) download page ↗
Done
Click this button to open VS Code
Open in VS Code
VS Code asks you to confirm; click Install
Done
Open the AI chat in VS Code in Agent mode
Done
Try it
Use DCC-MCP Core and tell me what you can do for me, then show me one thing
✓ If VS Code says it is using DCC-MCP Core or shows a DCC-MCP Core tool in the answer, you are connected

1 optional setting(s) (works without them)

The command above works without these. Add them only if you want to change something, following the maker’s description.

DCC_MCP_GATEWAY_PORT Port the machine-wide gateway binds. One agent endpoint fronts every live DCC backend, so the port in the transport URL has to match this value. · default 9765

Descriptions are as written by the maker.

Menu names checked against VS Code’s help page on Nov 11, 2026 · your screen may differ slightly by app version

Other apps that use MCP, such as Windsurf, Zed or Gemini CLI, each add servers differently. See how in the app list.

See every app that uses MCP →

JSON settings are under “For developers” below

Stuck? Ask people who use DCC-MCP Core💬 Discussion →

For developers: Terminal commands and JSON settings

Installed on your computer

Claude Code

claude mcp add --scope user dcc-core -- uvx dcc-mcp-server

Claude Desktop (claude_desktop_config.json)

{
  "mcpServers": {
    "dcc-core": {
      "command": "uvx",
      "args": [
        "dcc-mcp-server"
      ]
    }
  }
}

Cursor (~/.cursor/mcp.json)

{
  "mcpServers": {
    "dcc-core": {
      "command": "uvx",
      "args": [
        "dcc-mcp-server"
      ]
    }
  }
}

VS Code (mcp.json)

{
  "servers": {
    "dcc-core": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "dcc-mcp-server"
      ]
    }
  }
}

Values you fill in

DCC_MCP_GATEWAY_PORT env
Port the machine-wide gateway binds. One agent endpoint fronts every live DCC backend, so the port in the transport URL has to match this value.

Descriptions are as written by the maker.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

At a glance

GitHub stars i: 73
Downloads per weeki: 2,634/wk
Licence i: MIT
Connection: Runs on your computer
Transport: streamable-http
Latest versioni: 0.20.51
Categoryi: Other
Published to registryi: Oct 28, 2026
Code last changedi: Nov 14, 2026
Maker: dcc-mcp
Packages: pypi: dcc-mcp-server
Repo: dcc-mcp/dcc-mcp-core
Website: dcc-mcp.github.io
Registry status: Active

Which apps can use it

Based on how this server connects and on each app’s own docs, checked Nov 10, 2026.

Run on your computer in

Claude Goose LM Studio Msty Studio Cherry Studio Cursor VS Code + GitHub Copilot Visual Studio Windsurf (Devin Desktop)Zed Kiro Cline Continue JetBrains AI Assistant Claude Code Codex CLI Gemini CLI OpenCode nodecode Warp LibreChat Raycast

All apps, with pros and cons →

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 15, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account dcc-mcp, which owns the repo
✓ Pass
Still maintained
Code last changed yesterday
✓ Pass
Has a licence
Uses MIT
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Discussion

Ask, share a tip or report that it does not work. Sorted by what people marked helpful.

No posts yet. Be the first to ask or share.

Posts here are written by users, not the PopNx team.

User reviews

No reviews yet

Compared with others in Otheri

More stars than 98% of the other 19808 servers in this category
Published to the registry more recently than 70% of the other 19808 servers in this category
Basic checks equal to or better than 100% of the other 19808 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is DCC-MCP Core safe?

It passed 3 of 3 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed yesterday, and the latest version (0.20.51) reached the registry 18 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: World Monitor, Claude Flow, Windmill

Others in the same category

Servers in the same category from other makers, sorted by stars.

Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

World Monitor

Real-time global intelligence on conflict, markets, energy, trade, shipping and cyber. 97 tools.

by koala73Widely used★ 90.6KUpdated yesterday

Runs in the cloud✓ service owner · worldmonitor.app

How to connect

Claude Flow

AI orchestration with hive-mind swarms, neural networks, and 87 MCP tools for enterprise dev.

by ruvnetWidely used↓ 13.5K/wk★ 81.7KUpdated 14 months ago

Runs on your computer

Install

Windmill

Run Windmill scripts and flows and manage jobs, schedules, resources and variables.

by windmill-labsWidely used★ 18.6KUpdated 3 days ago

Runs in the cloud

How to connect

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

dcc-mcp-core

中文 | English

Website · Marketplace · Showcase · For Agents · Ecosystem

One MCP control plane for every DCC: an AI agent turns one prompt into a finished Blender material in 60 seconds.

pip install dcc-mcp-core

Try this → Blender material 60s Showcase: the shortest path from one prompt to a rendered material in a live Blender session, powered by dcc-mcp-blender. See Quick start: operate a DCC for the full CLI path.

Skill-first, Rust-powered control plane for a growing creative-tool ecosystem.

dcc-mcp-core connects agents to desktop DCCs, game engines, 2D tools, production systems, asset providers, profilers, and custom studio hosts through discoverable MCP and REST capabilities. It provides the gateway, Skills, structured results, main-thread dispatch, diagnostics, IPC, workflows, and packaged CLI/server binaries needed to operate real sessions.

One protocol, across every registered host. Every host adapter in this ecosystem implements the same MCP protocol and the same tool contract. The registry is the source of truth, and the count is reproducible from a checkout:

rg -c '^    tags: \[.*"adapter".*\]' dcc-mcp-catalog.yml

That reports 48 adapter entries in dcc-mcp-catalog.yml. See the complete generated host list for every current entry and its install metadata. The count includes experimental discovery-only PhotoCraft; it does not establish release-qualified support or automatic installation for every entry. Selected examples include:

Area	Selected hosts
Desktop DCC	Maya, Blender, Houdini, 3ds Max, Cinema 4D, Nuke, Katana, MotionBuilder, ZBrush, Mari, Substance 3D Designer, Substance 3D Painter, Marmoset
Engines and real-time	Unreal, Unity, Godot, Tiled, TouchDesigner
Design, image, video	Photoshop, Illustrator, GIMP, Krita, After Effects, Premiere, Kdenlive, PowerPoint, OBS, ComfyUI
CAD, procedural, interchange	FreeCAD, OpenSCAD, SketchUp, Material Maker, LiquiGen, OpenUSD
Production tracking and QA	Flow Production Tracking (ShotGrid), RenderDoc, Shogun, Wwise

The full list, with install metadata, is published in the ecosystem directory.

Three shared contracts keep it one matrix. Host coverage is only useful if the adapters stay interchangeable. Three contracts published by core are what stop host adapters from becoming unrelated servers:

Contract	What it guarantees	Where to verify
Core shared contract	One shared asset-import contract (`asset_import`), one discovery decision schema (`dcc-discovery-decision-v1`), and postcondition write-then-read-back so a tool proves its own result	`python/dcc_mcp_core/asset_import.py`, `contracts/dcc-discovery-decision-v1.schema.json`, `python/dcc_mcp_core/runtime/postcondition.py`
Recipe runtime	`recipes__list` / `recipes__get` / `recipes__validate` / `recipes__apply` driven by a declarative `RECIPES.yaml`, so a host ships capabilities as data instead of a bespoke server	`skills/mesh-ops/RECIPES.yaml`
Marketplace Skill contract	Every published entry carries `examplePrompts`; `Skills`, `Studio` and `Infrastructure` entries must also declare `recovery` and `undo`, so failures have a next step and edits have one rollback step	Enforced in dcc-mcp/marketplace by `validate_marketplace.py` — run `python scripts/validate_marketplace.py prompt-contract` (see `prompt-contract.md`)

How to compare us. Compare host coverage and contract completeness — both reproducible from the commands and files above. We do not claim a ranking, and we do not compare on stars or tool counts.

Features

Bundled agent skill. Every checkout and every release ships llms.txt — a compact index — and llms-full.txt, the full API reference. Both are machine-readable, so an agent can read them straight from the repository and start working without installing a Skill, browsing the marketplace, or scraping rendered docs.
Skills as the production unit. Ship capability as a versioned SKILL.md package with tools.yaml, progressive discovery, lint and schema validation, and hot reload (DccSkillHotReloader).
Main-thread dispatch. Actions are queued onto the DCC's main thread, so calls are safe against applications that crash when scripted off-thread.
Structured results and postconditions. Every call returns typed results, and the shared postcondition contract writes then reads back so a tool has to prove its own effect.
Multi-instance routing. One gateway fronts every live DCC session and publishes gateway://instances, so an agent talks to one endpoint no matter how many hosts are running.
UI Control as a fallback. For tools with no API at all, bounded screenshot-and-click automation brings them into the same control plane (DCC UI Control).

Choose your entry point

You want to…	Start with
Control a running DCC from an agent or CI job	Install the `dcc-mcp` Agent Skill, then use dcc-mcp-cli
Expose a DCC adapter over MCP/REST	create_skill_server
Start a server from a resolved package (Rez)	dcc-mcp-core serve
Add tools without Python registration code	SKILL.md + tools.yaml
Discover and install reusable Skills	DCC-MCP Marketplace
Build a new DCC adapter	new-adapter-onboarding.md
Understand routing and multi-instance behavior	gateway.md
Integrate from any HTTP client	rest-api-surface.md

The current contract

The default agent path is CLI + REST:

dcc-mcp-cli dcc-types (when adapter support is unclear)

-> list -> search -> describe -> load-skill (only when needed) -> call

The gateway keeps tools/list bounded. It advertises the canonical discovery/dispatch wrappers (search, describe, load_skill, and call) instead of fanning every backend tool into one large list. Backend capabilities are discovered by search, inspected by describe, and invoked through the wrapper or the REST twin (POST /v1/search, /v1/describe, /v1/call).

When a concrete DCC session is needed, read gateway://instances; each entry already includes its mcp_url. Do not use the removed legacy instance tools (list_dcc_instances, get_dcc_instance, or connect_to_dcc). Wrapper inputs belong inside arguments; do not put backend fields beside tool_slug.

For direct per-DCC MCP connections, the compatibility discovery names (search_tools, describe_tool, and call_tool) remain available where the server exposes them. New gateway integrations should use the canonical names above.

Why dcc-mcp-core exists

The shortest DCC agent demo asks a model to write and run a mayapy, hython, or Blender Python script. A production pipeline cannot depend on getting the right script from the model on every turn. Repeated code generation costs tokens, varies with the model and context, and leaves every adapter to rebuild transport, main-thread dispatch, validation, process lifecycle, routing, and diagnostics.

dcc-mcp-core moves that common engineering into one reusable control plane:

Layer	Reused capability
Integration	MCP and REST endpoints, Host RPC/IPC, typed schemas, resources, prompts, and structured results
DCC runtime	Main-thread affinity, readiness, multi-instance routing, async jobs, cancellation, checkpoints, workflows, artefacts, and UI Control
Skill delivery	`SKILL.md` packages, progressive discovery, lint/schema validation, hot reload, persistence, marketplace distribution, and project/team scopes
Operations	CLI, gateway, Admin UI, policies, audit records, traces, logs, metrics, health checks, and VRS replay

This project provides the infrastructure for agents to control DCC applications; it does not build or prescribe the agent itself. Agents and models will change, while studio interfaces, permission boundaries, and pipeline knowledge still need to be maintained. The framework turns those investments into reusable engineering assets.

Existing tools need AI access too

Adding an AI-facing interface to a new tool is usually straightforward. The harder problem is making years of existing tools usable by agents when they have no API, cannot be modified, or expose part of a workflow only through a window or modal dialog.

For that gap, dcc-mcp-core provides the bounded Computer Use-style DCC UI Control capability. It combines exact-window screenshots, semantic controls when available, scoped actions, waits, recording, policy checks, audit, and result verification. Native Skills/APIs remain the preferred path; UI Control lets legacy and interface-only workflows join the same agent control plane without pretending that every application has a clean programmable API.

MCP is an entry point, not the ceiling

MCP is the industry-standard agent interface we reuse, not the limit of the framework. A stable Python, C++, HTTP, command-port, or native plugin interface can be integrated under the same discovery, execution, safety, and operations contracts.

We also include useful vendor capabilities instead of replacing them. Unreal Engine 5.8 introduced an experimental built-in MCP server and Toolset Registry. The Unreal Official MCP Bridge enables and calls those official toolsets through DCC MCP without redistributing them. Vendor tools, DCC-MCP tools, and studio tools can share one agent-facing workflow.

Skills are the production unit

A Skill turns proven pipeline knowledge into a versioned, typed, testable, and distributable operation. A lower-cost model may struggle to invent scene-editing logic from scratch but remain effecti

Only the beginning is shown; read the rest in the repo

DCC-MCP Core

What it does

Install

Install Python (uv)

Open the Claude desktop app’s settings file

Paste these settings and save the file

Quit Claude and open it again

Try it

What you can do instead

Install Python (uv)

Open Terminal

Paste this command and press Enter

Type /mcp in Claude Code

Try it

Install Python (uv)

Click this button to open Cursor

Open the AI chat in Cursor in Agent mode

Try it

Install Python (uv)

Click this button to open VS Code

Open the AI chat in VS Code in Agent mode

Try it

Installed on your computer

Values you fill in

At a glance

Which apps can use it

Related guides

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Discussion

User reviews

Compared with others in Otheri

Common questions

Others in the same category

World Monitor

Claude Flow

Windmill

README from the maker

dcc-mcp-core

Features

Choose your entry point

The current contract

Why dcc-mcp-core exists

Existing tools need AI access too

MCP is an entry point, not the ceiling

Skills are the production unit