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Dcc MCP Rpyc

RPYC plugin system for DCC software, enabling remote procedure calls from external applications.

Unclaimed MIT last commit 7 months ago devtools
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About Dcc MCP Rpyc

Dcc MCP Rpyc is an MCP server published by loonghao in the Developer Tools category: rPYC plugin system for DCC software, enabling remote procedure calls from external applications. It has been installed 0 times through Conduid.

The repository has 1 stars and 0 forks, with the last commit 7 months ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues (0) before depending on it in production.

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npx dcc-mcp-rpyc

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Releases

2.0.0v2.0.0 · 7 Apr 2026[2.0.0](https://github.com/loonghao/dcc-mcp-ipc/compare/1.0.0...2.0.0) (2026-04-07) ⚠ BREAKING CHANGES migrate to dcc-mcp-core v0.12.0 Rust/PyO3 API Features align with dcc-mcp-core latest API…
0.4.00.4.0 · 1 Apr 2025dcc-mcp-rpyc 0.4.0 <div align="center"> [![PyPI version](https://badge.fury.io/py/dcc-mcp-rpyc.svg)](https://badge.fury.io/py/dcc-mcp-rpyc) [![Build…
0.3.00.3.0 · 31 Mar 2025dcc-mcp-rpyc 0.3.0 <div align="center"> [![PyPI version](https://badge.fury.io/py/dcc-mcp-rpyc.svg)](https://badge.fury.io/py/dcc-mcp-rpyc) [![Build…
0.2.10.2.1 · 24 Mar 2025dcc-mcp-rpyc 0.2.1 <div align="center"> [![PyPI version](https://badge.fury.io/py/dcc-mcp-rpyc.svg)](https://badge.fury.io/py/dcc-mcp-rpyc) [![Build…
0.2.00.2.0 · 24 Mar 2025dcc-mcp-rpyc 0.2.0 <div align="center"> [![PyPI version](https://badge.fury.io/py/dcc-mcp-rpyc.svg)](https://badge.fury.io/py/dcc-mcp-rpyc) [![Build…

README

DCC-MCP-IPC

PyPI version CI Python Version License Ruff

Multi-protocol IPC adapter layer for DCC software integration with Model Context Protocol (MCP).

Built on top of dcc-mcp-core (Rust/PyO3 backend), it provides a high-performance, type-safe framework for exposing DCC functionality as MCP tools across multiple transport protocols.

Documentation: docs site | v2.0.0 (Unreleased) — Breaking changes ahead, see CHANGELOG.md

Why DCC-MCP-IPC?

Feature Description
Protocol-agnostic RPyC for embedded-Python DCCs (Maya/Houdini/Blender), HTTP for Unreal/Unity, WebSocket, and Rust-native IPC for maximum throughput.
Zero-code Skills Drop a SKILL.md file into a directory — SkillManager auto-registers it as an MCP tool. No Python boilerplate needed.
Rust-powered core Action dispatch, validation, and telemetry handled by dcc-mcp-core via PyO3; Python layer focuses on DCC-specific glue code.
Hot-reload skills SkillWatcher monitors skill directories and re-registers tools on file changes without restarting the DCC.
Service discovery ZeroConf (mDNS) + file-based fallback for automatic DCC server detection.
Connection pooling ConnectionPool with auto-discovery for efficient client-side connection reuse.

Features

  • Thread-safe RPyC / HTTP / WebSocket / Rust-native IPC server implementations
  • Skills system — zero-code MCP tool registration from SKILL.md frontmatter with hot-reload
  • Action system backed by ActionRegistry + ActionDispatcher (Rust) — JSON-serialised parameter dispatch
  • Transport factory — pluggable transport layer (rpyc, http, websocket, ipc)
  • Service discovery: ZeroConf (mDNS) + file-based fallback via ServiceDiscoveryFactory
  • Async client (asyncio) for non-blocking operations
  • Abstract base classes for creating DCC-specific adapters (DCCAdapter) and services
  • Application adapter pattern for generic app integration
  • Scene & snapshot interfaces over RPyC and HTTP transports
  • Mock DCC services for testing without actual DCC applications
  • Comprehensive error handling with custom exception hierarchy

Architecture

graph TD
    A[AI Assistant / MCP Client] --> B[MCP Server Layer]
    B --> C[ActionAdapter\nActionRegistry + ActionDispatcher]
    C --> D[RPyC Transport\nDCC Python env]
    C --> E[IPC Transport\nRust FramedChannel]
    C --> F[HTTP Transport\nUnreal / Unity REST]
    C --> G[WebSocket Transport]
    G[SkillManager\nSKILL.md auto-discovery] --> C
    H[dcc-mcp-core\nRust/PyO3 backend] --> C
    D --> I[DCC App\nMaya / Houdini / Blender]
    E --> I
    F --> J[DCC App\nUnreal / Unity]

    style C fill:#e1f5fe
    style H fill:#fff3e0
    style G fill:#e8f5e9

Key Components

Component Module Description
ActionAdapter action_adapter.py Wraps Rust ActionRegistry + ActionDispatcher; registers handlers and dispatches JSON-parameterised calls
SkillManager skills/scanner.py Scans directories for SKILL.md skills, registers them as action handlers, supports hot-reload
DCCServer server/dcc.py Manages the RPyC/IPC server lifecycle inside a DCC process
BaseDCCClient client/base.py Core client connection/call logic with auto-discovery
ConnectionPool client/pool.py Connection pooling for efficient resource management
IpcClientTransport / IpcServerTransport transport/ipc_transport.py Rust-native framed-channel IPC, registered as "ipc" protocol
ServiceDiscoveryFactory discovery/factory.py Strategy-pattern selector for ZeroConf or file-based discovery
MockDCCService testing/mock_services.py Simulates DCC applications for testing

Installation

pip install dcc-mcp-ipc

With optional ZeroConf support:

pip install "dcc-mcp-ipc[zeroconf]"

Or with Poetry:

poetry add dcc-mcp-ipc

Requirements

  • Python >= 3.8 (< 4.0)
  • dcc-mcp-core >= 0.12.0 (< 1.0.0)
  • rpyc >= 6.0.0 (< 7.0.0)
  • Optional: zeroconf >= 0.38.0 for mDNS discovery

Quick Start

Server-side (within DCC application)

<augment_code_snippet path="README.md" mode="EXCERPT">

from dcc_mcp_ipc.server import create_dcc_server, DCCRPyCService


class MayaService(DCCRPyCService):
    def get_scene_info(self):
        return {"scene": "Maya scene info"}

    def exposed_execute_cmd(self, cmd_name, *args, **kwargs):
        pass


server = create_dcc_server(
    dcc_name="maya",
    service_class=MayaService,
    port=18812,
)
server.start(threaded=True)

</augment_code_snippet>

Client-side

<augment_code_snippet path="README.md" mode="EXCERPT">

from dcc_mcp_ipc.client import BaseDCCClient


client = BaseDCCClient("maya", host="localhost", port=18812)
client.connect()
result = client.call("get_scene_info")
client.disconnect()

</augment_code_snippet>

Usage Guide

Action System (v2.0.0+)

The Action system is built on the Rust-backed ActionRegistry + ActionDispatcher. All parameters are JSON-serialised:

<augment_code_snippet path="README.md" mode="EXCERPT">

from dcc_mcp_ipc.action_adapter import ActionAdapter, get_action_adapter


adapter = get_action_adapter("maya")


def create_sphere(radius: float = 1.0, name: str = "sphere1") -> dict:
    return {"success": True, "message": f"Created {name}", "context": {"name": name}}


adapter.register_action(
    "create_sphere",
    create_sphere,
    description="Create a sphere primitive",
    category="modeling",
    tags=["primitive", "mesh"],
)


result = adapter.call_action("create_sphere", radius=2.0, name="mySphere")
print(result.success)   # True
print(result.to_dict()) # {"success": True, ...}

</augment_code_snippet>

Zero-code Skills via SkillManager

Drop a SKILL.md file into a directory structure:

my_skills/
  create_light/
    SKILL.md      # frontmatter: name, description, tools, scripts
    run.py        # executed when the tool is called

<augment_code_snippet path="README.md" mode="EXCERPT">

from dcc_mcp_ipc.skills import SkillManager
from dcc_mcp_ipc.action_adapter import get_action_adapter


adapter = get_action_adapter("maya")
mgr = SkillManager(adapter=adapter, dcc_name="maya")

mgr.load_paths(["/pipeline/skills"])
mgr.start_watching()

# Now "create_light" is callable as an MCP tool
result = adapter.call_action("create_light", intensity=100.0)

</augment_code_snippet>

Connection Pool

<augment_code_snippet path="README.md" mode="EXCERPT">

from dcc_mcp_ipc.client import ConnectionPool


pool = ConnectionPool()

with pool.get_client("maya", host="localhost") as client:
    result = client.call("execute_cmd", "sphere", radius=5)
    print(result)

</augment_code_snippet>

Service Factories

Three factory patterns are available for different lifecycle needs:

<augment_code_snippet path="README.md" mode="EXCERPT">

from dcc_mcp_ipc.server import (
    create_service_factory,
    create_shared_service_instance,
    create_raw_threaded_server,
)


class SceneManager:
    def __init__(self):
        self.scenes = {}

    def add_scene(self, name, data):
        self.scenes[name] = data


scene_manager = SceneManager()

# Per-connection instance
service_factory = create_service_factory(MayaService, scene_manager)

# Shared singleton instance
shared_service = create_shared_service_instance(MayaService, scene_manager)

# Raw threaded server
server = create_raw_threaded_server(service_factory, port=18812)
server.start()

</augment_code_snippet>

Rust-native IPC Transport

Zero-copy low-latency messaging via the Rust core:

<augment_code_snippet path="README.md" mode="EXCERPT">

import os
from dcc_mcp_core import TransportAddress
from dcc_mcp_ipc.transport.ipc_transport import (
    IpcClientTransport,
    IpcServerTransport,
    IpcTransportConfig,
)

# Client side
config = IpcTransportConfig(host="localhost", port=19000)
transport = IpcClientTransport(config)
transport.connect()
result = transport.execute("get_scene_info")
transport.disconnect()

# Server side (inside DCC plugin)
def handle_channel(channel):
    msg = channel.recv()
    # process and respond ...

addr = TransportAddress.default_local("maya", os.getpid())
server = IpcServerTransport(addr, handler=handle_channel)
bound_addr = server.start()

</augment_code_snippet>

Creating a Custom DCC Adapter

<augment_code_snippet path="README.md" mode="EXCERPT">

from dcc_mcp_ipc.adapter import DCCAdapter
from dcc_mcp_ipc.client import BaseDCCClient


class MayaAdapter(DCCAdapter):
    def _initialize_client(self) -> None:
        self.client = BaseDCCClient(
            dcc_name="maya",
            host=self.host,
            port=self.port,
            connection_timeout=self.connection_timeout,
        )

    def create_sphere(self, radius: float = 1.0):
        self.ensure_connected()
        assert self.client is not None
        return self.client.execute_dcc_command(f"sphere -r {radius};")

</augment_code_snippet>

Async Client

<augment_code_snippet path="README.md" mode="EXCERPT">

import asyncio
from dcc_mcp_ipc.client.async_dcc import AsyncDCCClient


async def main():
    client = AsyncDCCClient("maya", host="localhost", port=18812)
    await client.connect()
    result = await client.call("get_scene_info")
    await client.disconnect()


asyncio.run(main())

</augment_code_snippet>

Testing with Mock Services

<augment_code_snippet path="README.md" mode="EXCERPT">

import threading
from dcc_mcp_ipc.testing.mock_services import MockDCCService
from dcc_mcp_ipc.client import BaseDCCClient


server = MockDCCService.start(port=18812)

client = BaseDCCClient("mock_dcc", host="localhost", port=18812)
client.connect()
info = client.get_dcc_info()
print(info)  # {"name": "mock_dcc", ...}
client.disconnect()
server.stop()

</augment_code_snippet>

Development

Setup

git clone https://github.com/loonghao/dcc-mcp-ipc.git
cd dcc-mcp-ipc
poetry install

Running Tasks

nox -s pytest          # Run tests
nox -s lint            # Lint (mypy + ruff + isort)
nox -s lint-fix        # Auto-fix lint issues
nox -s build           # Build distribution packages

Project Structure

dcc-mcp-ipc/
├── src/dcc_mcp_ipc/
│   ├── __init__.py              # Lazy-import public API surface
│   ├── action_adapter.py         # Action system (Rust-backed)
│   ├── adapter/                  # DCC & application adapters
│   ├── client/                   # Synchronous & async clients + pool
│   ├── server/                   # RPyC server + factories + lifecycle
│   ├── transport/                # RPyC / HTTP / WS / IPC transports
│   ├── discovery/                # ZeroConf + file-based service discovery
│   ├── skills/                   # SkillManager zero-code system
│   ├── scene/                    # Scene operations interface
│   ├── snapshot/                 # Snapshot interface
│   ├── application/              # Generic application adapter/service/client
│   ├── testing/                  # Mock services for testing
│   └── utils/                    # Errors, DI, decorators, RPyC helpers
├── tests/                        # 68 test files mirroring source layout
├── examples/                     # Usage examples
├── docs/                         # VitePress documentation site
└── nox_actions/                  # Nox task definitions

License

MIT

README mirrored from the source repository yesterday. The original is authoritative.

Questions

About Dcc MCP Rpyc

How do I install Dcc MCP Rpyc?

Run npx dcc-mcp-rpyc, then add the server to your MCP client's configuration. Conduid has recorded 0 installs, so the command is known to work with current clients.

Is Dcc MCP Rpyc safe to use with an AI agent?

Its trust score is 56 out of 100 (fair). It passes 0 of 1 static security checks; the failures are listed above. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Dcc MCP Rpyc still maintained?

Yes — the latest release is 2.0.0 (7 Apr 2026), and the last commit was 7 months ago. The repository has 1 stars and 0 open issues.