RPYC implementation for DCC software integration with Model Context Protocol
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# DCC-MCP-RPYC <div align="center"> <img src="https://raw.githubusercontent.com/loonghao/dcc-mcp-rpyc/main/logo.svg" alt="DCC-MCP-RPYC Logo" width="200"/> [](https://badge.fury.io/py/dcc-mcp-rpyc) [](https://github.com/loonghao/dcc-mcp-rpyc/actions) [](https://pypi.org/project/dcc-mcp-rpyc/) [](https://github.com/loonghao/dcc-mcp-rpyc/blob/main/LICENSE) [](https://github.com/psf/black) [](https://github.com/astral-sh/ruff) [](https://pepy.tech/project/dcc-mcp-rpyc) </div> [English](README.md) | [中文](README_zh.md) RPyC implementation for DCC software integration with Model Context Protocol (MCP). This package provides a framework for exposing DCC functionality via RPYC, allowing for remote control of DCC applications. ## Why RPyC? RPyC (Remote Python Call) offers significant advantages for DCC software integration: - **Dynamic Interface Exposure**: RPyC dynamically exposes interfaces within DCC applications, reducing development effort by eliminating the need to create static API wrappers. - **Native API Access**: Enables direct use of native DCC APIs like Maya's `cmds`/`pymel`, Houdini's `hou`, Blender's `bpy`, and Nuke's Python API without translation layers. - **Transparent Remote Execution**: Code written for local execution can run remotely with minimal changes, preserving the developer experience. - **Reduced Boilerplate**: Minimizes repetitive code needed for inter-process communication compared to other IPC methods. - **Object References**: Maintains l…
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