Manufacturers and engineers often start new digital workflows with an abstract diagram — a visual sketch of how data should connect across orders, materials, and production steps. Translating that model into a live, functioning data structure inside a manufacturing platform can take hours of manual setup.
Model Context Protocols (MCPs) are emerging as a key way to bridge that gap. An MCP defines how large language models communicate with real-world systems — giving AI structured access to tools, data, and APIs so it can perform real actions safely. Instead of operating in isolation, an MCP provides context: the model knows what it’s allowed to do, how to interpret data structures, and where to act. This makes it possible for AI to build, modify, or analyze systems in a controlled, auditable way.
Tulip’s Model Context Protocol (MCP) brings this concept into manufacturing. In this short demo, Pete Hartnett shows how Tulip’s MCP connects to a large language model through an interface like Claude to build a complete data structure from a simple diagram. The result: faster setup, fewer errors, and a direct path from concept to live application.
What You’ll Learn
This demo highlights a broader shift in how manufacturers can build digital systems. Instead of manually translating ideas into structured databases, engineers can now use natural language and existing visual assets to generate production-ready data models. MCP enables AI to perform real actions — such as creating, mapping, and validating tables — within defined boundaries, combining speed with governance.
For manufacturers, this means accelerating solution development without sacrificing accuracy or security. For engineers, it turns AI into a real collaborator: one that understands intent, follows process rules, and builds usable digital assets instantly.
Learn more about Tulip AI → tulip.co/platform/tulip-ai
Explore examples and connectors → Tulip Library: AI Tools and Connectors
Tulip AI is built for operations — powered by contextualized data and controlled by you.
Watch the full demo to see how the Tulip MCP connects intelligent models to real manufacturing data, enabling rapid setup and scalable AI-ready systems.
FAQ
What is the Model Context Protocol (MCP)?
An MCP is an open standard that allows AI systems to interact directly with external software and data through defined tools and APIs. It provides the context and constraints that make AI useful — and safe — in enterprise environments.
Can MCP work with different language models?
Yes. In Tulip’s demo, MCP connects to Gemini through Claude, but the protocol is model-agnostic. It can interface with any LLM that supports structured tool use and secure data exchange.
What kinds of tasks can MCP automate?
Anything that involves structured, rule-based configuration — such as building tables, managing app logic, or generating reports — can be handled by MCP-connected AI agents.
