How to Use the Kontent.ai MCP in AutoGen

Q: How do I configure AutoGen to use the Kontent.ai MCP Server?

Install autogen-extmcp and use the MCP server tools configuration with your Vinkius HTTP URL. Pass the generated tools array directly into the AssistantAgent constructor to give your agents access.

Q: Can AutoGen agents search for specific assets?

Yes, agents can call listcontentassets to locate images or documents. A media-focused agent can analyze these assets and debate whether they match the style guidelines of a specific content item.

Q: How do AutoGen agents handle localized content from Kontent.ai?

The agents use listprojectlanguages to inspect available locales. A translator agent can then coordinate with a reviewer agent to verify that localized versions of items fetched via getcontentitem are complete.

Let specialized AutoGen agents debate and audit your Kontent.ai schemas and content structures.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Kontent.ai MCP to AutoGen

Create your Vinkius account to connect Kontent.ai to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Kontent.ai with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Collaborative content audits

AutoGen allows multiple agents to review your headless setup. An editor agent can call `list_content_items` to find recent pages, while an SEO agent runs `search_kontent_ai` to inspect metadata and point out missing descriptions. They debate quality issues in a shared conversation loop. You get a detailed, automated audit report based on live production data without lifting a finger.

Schema validation debates

Before deploying frontend changes, you need to ensure your content models match your code. A developer agent can query `list_content_types` and `get_content_type_element` to inspect your current schema. A QA agent then compares these elements against your local TypeScript interfaces. The agents negotiate fixes, flagging mismatches before they break your production build.

Automated taxonomy alignment in AutoGen

Keeping content tags consistent across a large team is incredibly difficult. This MCP Server gives your AutoGen agents access to `list_taxonomies` and `get_taxonomy_group` to analyze your tagging structure. The agents compare current tags against your taxonomy rules. They can flag miscategorized items and suggest precise taxonomy updates directly in their chat log.

Setup guide

Set up Kontent.ai MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Kontent.ai tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Kontent.ai_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Kontent.ai data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Kontent.ai_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Kontent.ai data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Kontent.ai. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Kontent.ai now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Kontent.ai MCP in AutoGen

Install `autogen-ext[mcp]` and use the MCP server tools configuration with your Vinkius HTTP URL. Pass the generated tools array directly into the `AssistantAgent` constructor to give your agents access.

Yes, agents can call `list_content_assets` to locate images or documents. A media-focused agent can analyze these assets and debate whether they match the style guidelines of a specific content item.

The agents use `list_project_languages` to inspect available locales. A translator agent can then coordinate with a reviewer agent to verify that localized versions of items fetched via `get_content_item` are complete.

Yes, the setup uses the streamable HTTP transport provided by Vinkius. This allows your multi-agent cluster to communicate with the server over a secure, persistent connection.

Yes, all API requests to fetch content items, schemas, or taxonomies are routed through a secure, isolated V8 sandbox. Your delivery tokens are handled at the server level, keeping them invisible to the individual agents in the conversation.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript