Rhino MCP Server

Overview

Rhino MCP Server bridges the gap between AI assistants and Rhino 3D by exposing a Model Context Protocol (MCP) interface that lets Claude (or any MCP‑compatible client) create, modify, and interrogate 3D geometry directly from natural language. Rather than requiring a developer to write custom scripts or plug‑ins, the server translates high‑level AI commands into Rhino API calls, enabling a truly conversational design workflow.

The core problem this server solves is the friction that normally exists when integrating generative AI into CAD workflows. Designers often need to prototype ideas quickly, and the ability to ask an assistant to “create a box with a filleted edge” or “add a hole at the center of this surface” removes manual steps, reduces context switching, and keeps creative momentum. By exposing Rhino’s full object model through MCP, the server turns a text‑based prompt into an actionable command that Rhino executes in real time.

Key capabilities include:

• Object creation and manipulation – The server implements MCP tools for adding primitives, editing vertices, applying transformations, and performing boolean operations.
• State inspection – Clients can query the current scene for objects, layers, or material properties, allowing AI to reason about existing geometry before making changes.
• Real‑time feedback – Because the server runs inside Rhino, changes are immediately visible in the viewport, giving users instant visual confirmation of AI‑generated actions.
• Extensibility – The architecture separates the MCP logic (Python) from Rhino integration (C#), making it straightforward to add new tools or customize behavior without touching the core protocol stack.

In practice, Rhino MCP Server is invaluable for rapid prototyping, educational environments, and design research. A product designer can let Claude sketch out a concept by describing shapes verbally; an engineer can generate parametric parts from specifications; and students can experiment with geometry without learning Rhino’s command syntax. The server also supports batch workflows, where an AI script can generate a library of parts that are automatically added to a project.

Integration is seamless: after installing the Rhino plugin, developers simply point their MCP‑enabled assistant to the server’s address. The server then manages a socket connection, translating MCP messages into Rhino API calls via the C# plugin. This tight coupling ensures low latency and robust error handling, giving developers confidence that AI commands will execute reliably within the Rhino environment.