How to Use the Kling AI (Generative Video & Image) MCP in AutoGen

Q: How do I make an AutoGen agent use the Kling AI (Generative Video & Image) tools?

You use the mcpservertools function from the autogen-extmcp package. This fetches the tools from the server and makes them available to be registered with your AssistantAgent.

Q: Can multiple AutoGen agents collaborate on a single video from Kling AI (Generative Video & Image)?

Yes, that's the ideal setup. One agent can call texttovideo, pass the task ID to another agent in the conversation, and that second agent can be responsible for polling getvideotask.

Q: What happens to my prompts and images with AutoGen and the Kling AI server?

Your data, like text prompts or source image URLs for virtualtryon, is passed to the server for processing. Each request is executed within a secure, single-use sandbox on Vinkius. The server itself is ephemeral and doesn't persist your creative inputs.

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Setup Kling AI (Generative Video & Image) with AutoGen

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Assemble an AI Video Production Team

With AutoGen, you don't just call a tool; you create a team of agents that collaborate. An "Art Director" agent can write a prompt for `text_to_video`. A "Producer" agent then executes the call and polls `get_video_task`. A "Critic" agent can then review the result. If it's not good enough, it can instruct the Art Director to revise the prompt and try again. This conversational approach lets you automate complex creative decisions.

Debate Prompts Before Generating

The power of AutoGen is agent debate. You can have one agent propose a prompt for `text_to_image`, and another agent checks it against brand guidelines. They go back and forth until they agree on a final prompt before any API call is made to the MCP server. This saves you from generating bad or off-brand content. Use this for `virtual_try_on` by having one agent select a garment and another select a model, debating the best combination before calling the tool and starting a `get_tryon_task` poll.

The AutoGen MCP Server Tool Adapter

AutoGen's `McpToolAdapter` makes it simple to add the entire Kling AI toolset to your agents. Just point it at the MCP server URL, and your agents can start calling `text_to_video`, `image_to_video`, and more, right from their conversation. You can build specialized agents. For example, a "LipSync Specialist" agent whose only job is to receive a video and an audio file, execute the `lip_sync_video` tool, and return the final asset by polling `get_lipsync_task`.

Setup guide

Set up Kling AI (Generative Video & Image) 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 Kling AI (Generative Video & Image) 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="Kling AI (Generative Video & Image)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Kling AI (Generative Video & Image) 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="Kling AI (Generative Video & Image)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Kling AI (Generative Video & Image) data")
    print(result.messages[-1].content)

Get your connection token →

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Common questions about Kling AI (Generative Video & Image) MCP in AutoGen

You use the `mcp_server_tools` function from the `autogen-ext[mcp]` package. This fetches the tools from the server and makes them available to be registered with your `AssistantAgent`.

Yes, that's the ideal setup. One agent can call `text_to_video`, pass the task ID to another agent in the conversation, and that second agent can be responsible for polling `get_video_task`.

You can create a "Manager" agent that approves or denies tool calls based on a budget. Before the "Producer" agent calls `text_to_video`, it has to ask the Manager for permission, giving you control over API usage.

Definitely. You can set up a "Router" agent that receives a user request. Based on the request content, like if it contains an image URL, it directs the task to either a video generation agent or an image animation agent.

Your data, like text prompts or source image URLs for `virtual_try_on`, is passed to the server for processing. Each request is executed within a secure, single-use sandbox on Vinkius. The server itself is ephemeral and doesn't persist your creative inputs.

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