Kontent.ai MCP server

Martina Farkasova

3 minutes

The Kontent.ai MCP Server connects your Kontent.ai projects with AI-powered tools (like Claude, Cursor, or VS Code) using the Model Context Protocol (MCP). It allows AI to understand and work with your project’s content model and perform operations via natural language.

Understand the MCP server

The Model Context Protocol (MCP) is an open standard that standardizes how applications provide context to large language models (LLMs). Think of MCP as a universal connector, allowing AI models to interface with various tools and data sources through a standardized protocol. With the Kontent.ai MCP server

, you can:

Empower AI assistants to read your Kontent.ai content model structure and create new content types, snippets, and taxonomies
Accelerate content model creation by sending diagrams or descriptions that the AI interprets and implements
Integrate with multiple AI clients through a standard JSON configuration format

For more information on the protocol specification, visit modelcontextprotocol.io

How to run the MCP server

Prerequisites

Before setting up the MCP server, ensure you have:

Latest Node.js installed
A Kontent.ai Management API key with the Manage content model permission
Your Kontent.ai environment ID

Setup options

You can run the Kontent.ai MCP Server

using npx, choosing between STDIO or SSE transports. STDIO transport: Ideal for direct integration with AI tools that support STDIO communication.

Shell

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

SSE Transport: Suitable for scenarios requiring server-sent events (SSE) communication. The default port is 3001, but you can configure the port by setting the PORT environment variable.

Shell

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

Configuration

The server requires the following environment variables:

Variable	Description	Required
KONTENT_API_KEY	Your Kontent.ai Management API key	✅
KONTENT_ENVIRONMENT_ID	Your environment ID	✅
PORT	Port for SSE transport (defaults to 3001)	❌

Set these variables in your environment or a .env file.

Transport configuration

STDIO transport

To run the server with the STDIO transport, configure your MCP client with the following JSON:

JSON

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

SSE transport

To run the server with the SSE transport, follow these steps: 1. Start the server.

Shell

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

2. Set environment variables.

Shell

KONTENT_API_KEY=<management-api-key>
KONTENT_ENVIRONMENT_ID=<environment-id>
PORT=3001  # Optional; defaults to 3001

3. Configure your MCP client.

JSON

{
  "kontent-ai-sse": {
    "url": "http://localhost:3001/sse"
  }
}

Supported operations

The Kontent.ai MCP server exposes a set of tools that represent specific operations your AI assistant or application can perform on your Kontent.ai project. These operations align with your content modeling workflows, enabling AI to query, create, and manage:

Content types and their elements
Content type snippets
Taxonomy groups and terms
Languages

You can find the full set of available operations in the MCP Server GitHub repository

Common use cases

The examples below show how the MCP server tools empower AI assistants to provide meaningful, context-aware interactions, helping teams design, scale, and maintain content models efficiently.

Understand and extend a content model

These actions help AI assistants reason about the structure of the project and suggest or create new types based on user intent.

“What content types are already defined in this environment?”
“Add a new ‘Event‘ content type with elements for name, date, location, and description.”

Group elements for reuse with snippets

Content type snippets are ideal for maintaining consistency across content types while avoiding duplication. Assistants can define snippets once and insert them wherever needed, ensuring that updates are propagated automatically.

“Create an ‘SEO metadata’ content type snippet with title, description, and image elements.”

Organize content with taxonomies

Taxonomies provide structure for categorizing and filtering content. With the MCP Server, AI assistants can create and manage taxonomy groups and terms programmatically.

“Add a taxonomy group called ‘Industries’ with terms for Healthcare, Finance, and Education.”
“List all taxonomy groups defined in this environment.”

Explore language configuration

Understanding language settings helps the assistant make localization-aware modeling decisions, adapt to localized workflows, and offer content model recommendations based on supported languages.

“What languages are available in this environment?”