MCP and KOMPAS-3D: AI Agents in CAD via Python and the COM API

The long-held goal of automating routine work in engineering design is starting to become reality. Neural networks trained to write code have long stayed cut off from the real world of specialized software. MCP (Model Context Protocol) became the bridge connecting AI to tools, but until recently engineering software was left out. This project aims to fix that by giving neural networks direct access to the KOMPAS-3D API.

KOMPAS-3D is a powerful computer-aided design (CAD) system widely used in mechanical engineering, instrument making, and construction. Its API lets developers build their own applications and extensions that automate various aspects of design. Integrating it with an LLM (Large Language Model) via MCP opens up fundamentally new possibilities. Instead of hand-writing code for every automation task, users can now have a neural network generate that code from text instructions.

At its core, the project is an interface that lets an LLM interact with KOMPAS-3D through Python and the COM API. Python serves as the intermediary language, while the COM API provides access to the CAD system's functionality. The LLM receives a task as a text prompt and generates Python code, which then runs in KOMPAS-3D via the COM API. For example, you can ask the neural network to build a parametric part model from given dimensions and constraints, or to automatically generate a drawing from a 3D model.

This kind of integration has enormous potential. First, it can significantly cut the time spent on routine operations. Second, it opens CAD to users who lack deep programming knowledge. Third, it makes it possible to build intelligent tools that can adapt to changing requirements and generate optimal solutions. For example, a neural network can automatically optimize a part's design against specified criteria such as strength, weight, or cost.

There are challenges, however. Interaction between the LLM and the CAD system has to be secure and reliable. The neural network must generate correct code that does not cause errors or crash the system. Privacy also needs attention, especially when working with sensitive data. Developers must build in safeguards against unauthorized access and data leaks.

Overall, integrating LLMs with KOMPAS-3D via MCP is an important step toward intelligent CAD systems. It opens new horizons for automating engineering design and lets engineers focus on more creative and strategic work. Looking ahead, we can expect new tools and applications that use AI to optimize designs, generate drawings, and automate various aspects of the design process.

This direction has enormous potential to transform the engineering industry by making design processes more efficient, accessible, and innovative. Developing such technologies will help speed up new product development and boost the competitiveness of Russian companies.