Connect content operations to the AI ecosystem with the Kontent.ai MCP server

The extremely rapid growth of AI tooling has created new opportunities for content management, along with some critical challenges. Organizations that can successfully integrate their content operations with AI-powered workflows will have a decisive competitive advantage. Those that can’t face the risk of being left behind.

The Model Context Protocol (MCP) is emerging as the essential standard that makes this type of integration possible. While this standard is new, adopting it is quickly becoming essential for organizations that want to thrive in the AI era. Kontent.ai is proud to now offer an MCP server, making it easy to unlock entirely new types of workflows that were impossible before.

In this article, we’ll take a look at what the MCP and the Kontent.ai MCP server are, how they can help support new, more efficient types of workflows for content operations, and what the future might look like. But first, we’ll see why it’s important for AI to understand your content in the first place. Let’s get started!

Why does AI need to understand your content?

Content management systems are the foundation of modern digital experiences. But until now, they’ve largely been invisible to the AI systems we increasingly rely on, including copilots, agents, and orchestration tools.

At Kontent.ai, we believe this is the next frontier: making your content management system, your content structures, your workflows, and your entire content operations understandable, navigable, and actionable by AI.

The MCP server enables entirely new types of workflows that were prohibitively difficult, or in some cases impossible, to achieve before:

• Advanced automation. Using platforms like n8n to create sophisticated automations that pull content data from Kontent.ai, combine it with data from Salesforce, Hubspot, or other systems, and trigger intelligent actions across your entire tech stack.
• Conversational content operations. Ask Claude or ChatGPT to “show me all blog posts that mention our new product launch and create a summary report that also includes our latest sales data from Salesforce.
• Cross-platform orchestration. Use AI agents that can work with your content in Kontent.ai while simultaneously accessing web analytics, customer data, and other business systems.
• Automated content maintenance. Use AI agents that understand your content strategy and can optimize content operations without human intervention.
• Rapid content model prototyping. Describe a new content model using natural language, and have AI build it directly in your Kontent.ai project.

MCP is a gateway to an agentic future for our product, and we’re ready to help drive that future forward. I’m looking forward to where this leads—how it will shape our product, our customers, and the way we work.

Peter Skoda

Engineering Manager, Kontent.ai

By exposing structured content to AI systems through open protocols, developers and content teams can create seamless integrations between content operations and broader business processes. This enables a new level of efficiency for organizations that serves as a fundamental competitive advantage.

The MCP server is an important first step in this direction. Now, let’s explore exactly what the MCP server is.

What is the MCP? And what is an MCP server?

The model context protocol is a standard that lets AI tools (like Claude, Cursor, make, and n8n) interface with structured data systems (like Kontent.ai) in a standardized way. A common example is that the MCP is analogous to a USB hub: it allows different systems to connect and talk with each other.

The Kontent.ai MCP server is a purpose-built server that lets you connect Kontent.ai to any AI-based tools or platforms that support the model context protocol. This opens up all new types of possibilities, like chatting with Claude to help rapidly prototype a new content model in Kontent.ai, or using AI automation sequences in n8n to intelligently adjust content based on real-time business data.

Right now, the Kontent.ai MCP server supports reading and creating content types, snippets, and taxonomies. So you can, for example, show Claude a sketch of a new content type you want to create, and it can then create that content type in your Kontent.ai project.

It introduces a whole new way of working with Kontent.ai, and clears the way for many exciting possibilities for AI-powered content workflows.

Why it’s important

For technical content teams, dealing with all the facets of complex content operations is a time-consuming process. There’s a lot of value in experimentation, but the friction of manual setup gets in the way. The MCP server dramatically reduces that barrier with:

• Rapid prototyping. Use AI to generate a full content model from a sketch or diagram, JSON schema, or even a well-written description. Then iterate quickly and validate new ideas in seconds.
• Developer-native workflows. With direct integration into tools like n8n, Cursor, and Claude Desktop, you can interface with Kontent.ai in the tools you’re already using for work. No need to switch between platforms and lose context.
• Conversational content management. Since the MCP server allows for the manipulation of content types, snippets, and taxonomies through a conversational interface, complex content tasks (like content modeling) become more accessible and collaborative.

MCP and AI-powered prototyping move us closer to the future of content management: where more time is spent shaping great content strategies that produce better quality content, and less time is spent digging through API docs or implementation headaches.

Michael Berry

Director of Consulting Services, Kontent.ai

Success in the AI era requires new capabilities that aren’t available with traditional content management systems. Organizations need to be able to integrate their content operations with AI agents, tools, and automation platforms, and the MCP server makes this possible.

What you can do right now

The Kontent.ai MCP server can already:

• Read and write content types
• Create content type snippets
• Manage taxonomies
• List languages configured in your environment

And you can interact with all of these via your AI tools of choice. For example, you can paste a screenshot of a new content model and ask your AI assistant to create it in Kontent.ai. Or, you can just write a prompt, e.g. “Create a blog post content type with title, URL slug, author, publish date, update date, and body.” And then, thanks to the MCP server, you’ll find that content type in your Kontent.ai environment.

How does it work?

The server is open source, and you can check out all the details in the Github repository. It’s powered by Node.js and supports both STDIO and SSE transports, which make it flexible for different environments and workflows.

To run the server using STDIO transport:

npx @kontent-ai/mcp-server@latest stdio

Or use it with a global config in tools like VS Code, Claude Desktop, or Cursor using this configuration:

{
  "mcpServers": {
    "kontent-ai": {
      "command": "npx",
      "args": [
        "-y",
        "@kontent-ai/mcp-server@latest",
        "stdio"
      ],
      "env": {
        "KONTENT_API_KEY": "<kontent-ai-MAPI-key>",
        "KONTENT_ENVIRONMENT_ID": "<kontent-ai-environment-id>"
      }
    }
  }
}

For a full guide on getting started, head to the Github repository.

What the future holds

The model context protocol is quite new, but adoption among AI tool providers has been quite strong. With the  release of the Kontent.ai MCP server, Kontent.ai is showing its commitment towards helping to build the future of content operations. In the upcoming months, we’ll continue expanding what’s possible via the MCP server. Some possibilities include:

• Full content lifecycle support. Imagine going from a sketch on paper to a full-blown content model in your project, which could also include prepared content items like fully-written articles. This would make prototyping and implementing new content initiatives extremely fast and simple.
• Intelligent content model analysis. Your AI assistant could analyze your content ecosystem, identify gaps, and suggest optimization opportunities. It could even implement some of these changes automatically.
• Workflow automation. AI agents could help orchestrate complex content operations, including automated content updates and managing approval processes.

Get started today

Ready to get started? Try out the MCP server today and see what’s possible when you can connect your favorite AI tools to Kontent.ai. We’re sure the MCP server will help lead to faster, more flexible, and more creative ways of working.

The best place to start is right on Github. We can’t wait to see what you do with the new MCP server!