dcc-mcp-core
Foundational library for the DCC Model Context Protocol (MCP) ecosystem
Decision gist · record as of 2026-08-14
Yes, if you are building or extending an agent-driven creative pipeline. dcc-mcp-core is actively maintained (released 1 day ago), MIT-licensed, and eliminates the need to rebuild common DCC integration infrastructure. Beta status and a growing ecosystem of adapters make it a solid foundation for studios and tool developers. Install friction is moderate due to compiled wheels, but Python 3.7+ support is broad. No known security vulnerabilities.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires dcc-mcp-server as a runtime dependency; compiled wheels available for Python 3.7+ on Linux and Windows x86_64.
- Released 1 day ago with active maintenance.
- Medium install friction due to compiled wheels (cp37–cp37m, win_amd64 variants present), but supports Python 3.7–3.14 and depends on only one runtime package (dcc-mcp-server).
License · maintenance · safety
MIT (permissive) — MIT license (permissive) places no restrictions on commercial or proprietary use, modification, or distribution—standard permissive terms suitable for production integration.
last release 2026-08-13 (1 days) · last repo commit 2026-08-13 · 39 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 146,329 downloads/mo, #11,099 on PyPI
Alternatives
Verify before relying
pip install dcc-mcp-core
from dcc_mcp_core import create_skill_server
server = create_skill_server()
server.run()- Whether dcc-mcp-server is packaged separately or must be installed alongside dcc-mcp-core.
- Specific DCC versions and platforms supported by each adapter (Maya, Blender, Houdini, etc.).
- Performance characteristics and concurrency limits for multi-instance DCC routing.
- Whether the Admin UI and marketplace integration are included in this package or require separate installation.
What it is and what it does
dcc-mcp-core is a foundational library that bridges agents and AI models to desktop creative applications—DCCs like Maya, Blender, and Houdini, as well as game engines and 2D tools—through a unified MCP (Model Context Protocol) and REST interface. It handles the infrastructure that would otherwise be rebuilt for each adapter: main-thread dispatch, multi-instance routing, tool discovery, structured result validation, and lifecycle management.
The package centers on Skills—versioned, testable, distributable operations that encode pipeline knowledge without requiring repeated code generation. A studio TD or TA can compose Skills from SKILL.md, tools.yaml, and existing scripts, scoped to a project or team, then distribute them through a marketplace. The control plane keeps ownership of host connectivity, safety, and observability while letting teams customize the actual workflow logic. It also provides a bounded Computer Use-style UI Control capability for legacy tools that have no programmatic API.
Use it for
- Control a running Maya, Blender, or Houdini session from an agent or CI job using the CLI and REST gateway.
- Expose a custom DCC adapter or legacy tool over MCP/REST without rebuilding transport and dispatch infrastructure.
- Package and distribute reusable Skills (e.g., asset providers, rigging, procedural authoring) through a marketplace.
- Route agent requests to the correct DCC instance and handle main-thread affinity, async jobs, and cancellation.
- Integrate UI automation and Computer Use workflows for tools that lack a clean programmatic API.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building or extending an agent-driven creative pipeline.
dcc-mcp-core is actively maintained (released 1 day ago), MIT-licensed, and eliminates the need to rebuild common DCC integration infrastructure. Beta status and a growing ecosystem of adapters make it a solid foundation for studios and tool developers. Install friction is moderate due to compiled wheels, but Python 3.7+ support is broad. No known security vulnerabilities.
Install
dcc-mcp-core on PyPI
Before you install
Released 1 day ago with active maintenance. Medium install friction due to compiled wheels (cp37–cp37m, win_amd64 variants present), but supports Python 3.7–3.14 and depends on only one runtime package (dcc-mcp-server). Beta status and recent release suggest ongoing development.
Requires dcc-mcp-server as a runtime dependency; compiled wheels available for Python 3.7+ on Linux and Windows x86_64.
License in practice
MIT license (permissive) places no restrictions on commercial or proprietary use, modification, or distribution—standard permissive terms suitable for production integration.
Quickstart
pip install dcc-mcp-core
from dcc_mcp_core import create_skill_server
server = create_skill_server()
server.run()Verify before relying
- Whether dcc-mcp-server is packaged separately or must be installed alongside dcc-mcp-core.
- Specific DCC versions and platforms supported by each adapter (Maya, Blender, Houdini, etc.).
- Performance characteristics and concurrency limits for multi-instance DCC routing.
- Whether the Admin UI and marketplace integration are included in this package or require separate installation.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagedcc-mcp-server |
| Maintenance | Actively maintained 1 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 146,329 / month, #11,099 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Programming Language :: RustTopic :: Multimedia :: Graphics :: 3D ModelingTopic :: Software Development :: Libraries |
Evidence: dcc_mcp_core-0.20.6-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; dcc_mcp_core-0.20.6-cp37-cp37m-win_amd64.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “dcc agent control plane”
- dcc-mcp-coredcc-mcp-core provides a Rust-powered control plane that connects…
- dcc-mcp-serverDistributes a Rust-compiled CLI binary that runs as a standalone…
- ircProvides a low-level, event-driven IRC protocol implementation with…
Give your agent the search over MCP, or paste the wish link into any chat.
More Libraries packages
urllib3 is an HTTP client library that provides thread-safe connection pooling, SSL/TLS verification, multipart file uploads, request retries, compression support, and proxy handling for Python applications.
Requests is a Python HTTP library that simplifies sending HTTP/1.1 requests with automatic handling of headers, authentication, cookies, and response parsing.
Pluggy provides a plugin system that lets you define hook specifications and register implementations to be called in sequence, enabling extensible Python applications without tight coupling.
Install it if you're building an extensible application or framework.
Provides parsing, arithmetic, and recurrence rule computation for dates and times, with timezone support and iCalendar RFC compliance.
Install it if you need to parse flexible date strings, compute relative dates, handle timezones, or work with recurrence rules—it's the de facto choice for these tasks.
Six provides utility functions to write Python code that runs on both Python 2.7 and Python 3.3+, smoothing over language differences between the two versions.
pytest is a testing framework that lets you write test functions using plain assert statements and automatically discovers and runs them, with detailed failure reporting.
See also dcc-mcp-server · fast-agent-mcp · blender-mcp · irc · omnibase_core · deepagents-cli · nvidia-nat-mcp · godot-ai · composio-core · microsoft-fabric-rti-mcp