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Connect to Kontent.ai to manage content, types, taxonomies, and workflows via natural language
Connect to Kontent.ai to manage content, types, taxonomies, and workflows via natural language
Valid MCP server (2 strong, 2 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.
7 files analyzed ยท 1 issue found
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Set these up before or after installing:
Environment variable: KONTENT_API_KEY
Environment variable: KONTENT_ENVIRONMENT_ID
Environment variable: PORT
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-kontent-ai-mcp-server": {
"env": {
"PORT": "your-port-here",
"KONTENT_API_KEY": "your-kontent-api-key-here",
"KONTENT_ENVIRONMENT_ID": "your-kontent-environment-id-here"
},
"args": [
"-y",
"@kontent-ai/mcp-server"
],
"command": "npx"
}
}
}From the project's GitHub README.
Transform your content operations with AI-powered tools for Kontent.ai. Create, manage, and explore your structured content through natural language conversations in your favorite AI-enabled editor.
Kontent.ai MCP Server implements the Model Context Protocol to connect your Kontent.ai projects with AI tools like Claude, Cursor, and VS Code. It enables AI models to understand your content structure and perform operations through natural language instructions.
Before you can use the MCP server, you need:
You can run the Kontent.ai MCP Server with npx:
npx @kontent-ai/mcp-server@latest stdio
npx @kontent-ai/mcp-server@latest shttp
The server supports two modes, each tied to its transport:
| Transport | Mode | Authentication | Use Case |
|---|---|---|---|
| STDIO | Single-tenant | Environment variables | Local communication with a single Kontent.ai environment |
| Streamable HTTP | Multi-tenant | Bearer token per request | Remote/shared server handling multiple environments |
Configure credentials via environment variables:
| Variable | Description | Required |
|---|---|---|
| KONTENT_API_KEY | Your Kontent.ai key | โ |
| KONTENT_ENVIRONMENT_ID | Your environment ID | โ |
| appInsightsConnectionString | Application Insights connection string for telemetry | โ |
| projectLocation | Project location identifier for telemetry tracking | โ |
| manageApiUrl | Custom base URL (for preview environments) | โ |
For the Streamable HTTP transport, credentials are provided per request:
/{environmentId}/mcpAuthorization: Bearer <api-key>This allows a single server instance to handle requests for multiple Kontent.ai environments without requiring credential environment variables.
| Variable | Description | Required |
|---|---|---|
| PORT | Port for HTTP transport (defaults to 3001) | โ |
| appInsightsConnectionString | Application Insights connection string for telemetry | โ |
| projectLocation | Project location identifier for telemetry tracking | โ |
| manageApiUrl | Custom base URL (for preview environments) | โ |
To run the server with STDIO transport, configure your MCP client with:
{
"kontent-ai-stdio": {
"command": "npx",
"args": ["@kontent-ai/mcp-server@latest", "stdio"],
"env": {
"KONTENT_API_KEY": "<management-api-key>",
"KONTENT_ENVIRONMENT_ID": "<environment-id>"
}
}
}
Streamable HTTP transport serves multiple Kontent.ai environments from a single server instance. Each request provides credentials via URL path parameters and Bearer authentication.
First start the server:
npx @kontent-ai/mcp-server@latest shttp
Create a .vscode/mcp.json file in your workspace:
{
"servers": {
"kontent-ai-multi": {
"uri": "http://localhost:3001/<environment-id>/mcp",
"headers": {
"Authorization": "Bearer <management-api-key>"
}
}
}
}
For secure configuration with input prompts:
{
"inputs": [
{
"id": "apiKey",
"type": "password",
"description": "Kontent.ai API Key"
},
{
"id": "environmentId",
"type": "text",
"description": "Environment ID"
}
],
"servers": {
"kontent-ai-multi": {
"uri": "http://localhost:3001/${inputs.environmentId}/mcp",
"headers": {
"Authorization": "Bearer ${inputs.apiKey}"
}
}
}
}
Update your Claude Desktop configuration file:
~/Library/Application Support/Claude/claude_desktop_config.json%APPDATA%\Claude\claude_desktop_config.json~/.config/Claude/claude_desktop_config.jsonUse mcp-remote as a proxy to add authentication headers:
{
"mcpServers": {
"kontent-ai-multi": {
"command": "npx",
"args": [
"mcp-remote",
"http://localhost:3001/<environment-id>/mcp",
"--header",
"Authorization: Bearer <management-api-key>"
]
}
}
}
Add the server using the CLI:
claude mcp add --transport http kontent-ai-multi \
"http://localhost:3001/<environment-id>/mcp" \
--header "Authorization: Bearer <management-api-key>"
Note: You can also configure this in your Claude Code settings JSON with the
urlandheadersproperties.
[!IMPORTANT] Replace
<environment-id>with your Kontent.ai environment ID (GUID) and<management-api-key>with your key.
# Clone the repository
git clone https://github.com/kontent-ai/mcp-server.git
cd mcp-server
# Install dependencies
npm ci
# Build the project
npm run build
# Start the server
npm run start:stdio # For STDIO transport
npm run start:shttp # For Streamable HTTP transport
# Start the server with automatic reloading (no need to build first)
npm run dev:stdio # For STDIO transport
npm run dev:shttp # For Streamable HTTP transport
src/ - Source code
tools/ - MCP tool implementationsclients/ - Kontent.ai API client setupschemas/ - Data validation schemasutils/ - Utility functions
errorHandler.ts - Standardized error handling for MCP toolsthrowError.ts - Generic error throwing utilityserver.ts - Main server setup and tool registrationbin.ts - Single entry point that handles both transport typesFor debugging, you can use the MCP inspector:
npx @modelcontextprotocol/inspector -e KONTENT_API_KEY=<key> -e KONTENT_ENVIRONMENT_ID=<env-id> node path/to/build/bin.js
Or use the MCP inspector on a running streamable HTTP server:
npx @modelcontextprotocol/inspector
This provides a web interface for inspecting and testing the available tools.
To release a new version:
npm version [patch|minor|major] - this updates package.json, package-lock.json, and syncs to server.jsonMIT
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