Introducing Tulip MCP Server: Bridging Generative AI and Operations

We’re excited to announce the launch of Tulip’s Model Context Protocol (MCP) server, a new integration tool that connects large language models (LLMs) to your manufacturing data and workflows. This MCP server enables AI to interface with your Tulip instance, opening up a world of intelligent, context-aware interactions in production.

The Model Context Protocol (MCP) is an open standard that enables AI systems to interact with external tools and data sources consistently and structurally. Backed by industry leaders such as Anthropic, Google, and Microsoft, the MCP is rapidly becoming a fundamental layer for secure and scalable AI integration.

An MCP acts as specialized middleware, enabling Large Language Models (LLMs) to communicate effectively with external manufacturing systems and complete tasks using “tools” as defined by the MCP. Unlike traditional API specifications, MCPs provide richer contextual insights, facilitating precise, actionable interactions tailored specifically to manufacturing environments. The MCP serves as a crucial translator, bridging sophisticated AI reasoning and complex manufacturing operations.

Tulip’s MCP server is our official implementation of the Model Context Protocol (MCP) for the Tulip platform.

The MCP acts as a secure, real-time bridge between large language models (LLMs) and your Tulip instance. This allows AI to read data from and take actions in Tulip through the governed Tulip API. The MCP server makes a variety of Tulip functionalities, such as stations, machines, users, and tables, available as "tools" that an AI agent can use.

See the list of MCP tools →

In practice, this means an AI assistant can retrieve production metrics, create or update records, or trigger events in response to a user’s natural language request — all through Tulip's access-controlled API layer.

Why did we build it? We developed the MCP server to enable powerful use cases that integrate Tulip's real-time operational data with the capabilities of LLMs. Before MCP, integrating an AI assistant with Tulip often required custom scripts or manual data exports. Now, with a standardized MCP interface, AI can become an extension of the Tulip platform. This allows you to automate tasks and gain insights without extensive integration work. The goal is to make AI "Tulip-aware" and to make Tulip-powered operations more intelligent.

Tulip MCP improves operational efficiency by reducing manual overhead and repetitive tasks while providing instant access to critical manufacturing data.

For instance, a production supervisor can quickly obtain detailed status updates on particular production orders by querying Tulip tables. This allows for real-time awareness and immediate decision-making.

Similarly, quality managers can quickly identify frequent defects by using MCP's aggregation capabilities. This enables them to proactively intervene and maintain high-quality standards.

Tulip MCP also streamlines platform setup and provisioning tasks, enabling IT or operations engineers to swiftly configure new production stations. MCP can automatically create stations, assign necessary interfaces, configure apps, and update and create tables by parsing input data. These features reduce setup times and help users accomplish more within the platform.

For those eager to experiment, we suggest starting with a simple scenario: use the MCP server with an AI assistant to fetch a list of Tulip Tables, or to add a new record to a test table – you’ll get a feel for how the AI uses a tool (e.g., “List Tables”) and how the server responds with the data. Our Knowledge Base documentation provides several examples to showcase the syntax and possibilities.

Setting up your Tulip MCP involves a simple four-step process:

1. Download Tulip MCP – Clone the MCP repository and install dependencies.

2. Configure Your MCP – Populate a .env file with Tulip API credentials and workspace information.

3. Run the MCP server – Launch the server via npm start for production or npm run dev for development.

4. Connect to MCP – Integrate Tulip MCP with MCP-compatible clients using standard configurations.

If you’re new to MCP tools and AI, our support articles in the Tulip Knowledge Base and GitHub repository have you covered.

All MCP features are secured by Tulip’s API permissions. You control what the AI can do based on the API token scopes that you provide to the MCP server. For example, if you only grant read access to tables, the AI will not be able to create or delete records, and the server will safely return an error if asked to do so. This ensures that, although the AI is powerful, it operates within the guardrails you define.

Since it's built on a universal protocol, you have flexibility in how you use it. For example, you can run the MCP server on a local machine for a quick test with an AI coding assistant or deploy it on a server alongside your Tulip instance to provide continuous AI integration. The MCP server is compatible with popular AI development environments and agents.

For a hands-on look, reach out to us or your Tulip representative – we’d be happy to show you how an AI interacting with Tulip can fit into your workflow as a practical advantage you can start leveraging today.

Rather than generic AI hype, Tulip is focused on practical, safe, and effective AI solutions built for operations. The MCP server is an open-sourced foundation we will continue to build on through our Innovation Hub – expect more integrations and features as we learn from your use cases.