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

The dream of automating routine tasks in engineering design is becoming a reality. Neural networks trained to write code have long remained isolated from the real world of specialized software. The Model Context Protocol (MCP) became a bridge connecting AI with tools, but until recently, engineering software remained on the sidelines. This development represents an attempt to correct this situation by providing neural networks with direct access to the KOMPAS-3D API.

KOMPAS-3D is a powerful computer-aided design (CAD) system widely used in mechanical engineering, instrumentation, and construction. Its API allows developers to create custom applications and extensions that automate various aspects of design. Integration with LLM (Large Language Model) through MCP opens up fundamentally new possibilities. Now, instead of manually writing code for each automation task, you can use a neural network to generate this code based on text instructions.

The essence of the development lies in creating an interface that allows an LLM to interact with KOMPAS-3D through Python and COM API. Python serves as an intermediary language, while COM API provides access to CAD functionality. The LLM receives a task in the form of a text request, generates Python code, which is then executed in KOMPAS-3D through COM API. For example, you can ask a neural network to create a parametric model of a part according to given dimensions and constraints, or automatically generate a drawing based on a 3D model.

The implementation of such integration has enormous potential. First, it allows you to significantly reduce the time spent on routine operations. Second, it opens access to CAD for users who do not have deep programming knowledge. Third, it allows you to create intelligent tools capable of adapting to changing requirements and generating optimal solutions. For example, a neural network can automatically optimize a part's design according to specified criteria, such as strength, weight, or cost.

However, there are certain challenges. It is necessary to ensure the security and reliability of interaction between the LLM and CAD. It is important that the neural network generates correct code that does not lead to errors or system failures. It is also necessary to consider confidentiality issues, especially when working with sensitive data. Developers must provide mechanisms to protect against unauthorized access and information leaks.

In general, the integration of LLM with KOMPAS-3D through MCP represents an important step toward creating intelligent CAD systems. It opens new horizons for automating engineering design, allowing engineers to focus on more creative and strategic tasks. In the future, we can expect the emergence of new tools and applications that use AI capabilities to optimize designs, generate drawings, and automate various aspects of design.

This direction has enormous potential for transforming the engineering industry, making design processes more efficient, accessible, and innovative. The development of such technologies will help accelerate the development of new products and increase the competitiveness of Russian enterprises.