How to Use the Fliki (AI Text-to-Video & Speech API) MCP in AutoGen

Q: How do AutoGen agents collaborate on video creation?

One agent drafts the script, another runs listvoices to pick the voice, and a third agent executes generatevideo to render the final asset.

Q: How does the video generation agent check progress in AutoGen?

The agent uses the getvideo tool with the job ID returned by the generator. It can run this check periodically until the status changes to completed.

Q: What happens if a Fliki (AI Text-to-Video & Speech API) render fails in AutoGen?

Your monitoring agent will detect the failure via getvideo and can ask the writer agent to adjust the script parameters before retrying the generation.

Build multi-agent debate loops to write scripts, select voices, and render videos in AutoGen.

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Connect Fliki (AI Text-to-Video & Speech API) MCP to AutoGen

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Setup Fliki (AI Text-to-Video & Speech API) with AutoGen

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Coordinate script writers and video generators

The `generate_video` tool acts as the final execution step for your writing agents. In AutoGen, a scriptwriter agent drafts the text, an editor agent refines it, and a video manager agent calls this tool to start the render. This multi-agent setup ensures that the script is fully polished before any API calls are made. You avoid wasting API credits on poorly formatted scripts.

Let agents debate voice selection before rendering

The `list_voices` tool provides the raw voice data that your agents can analyze. One agent can select a voice, while another checks if the voice matches the emotional tone of the script. Once the agents agree on the best voice, the selected ID is passed to the video generator. This consensus-driven approach keeps your brand voice consistent.

Track video status with this MCP Server

The `get_video` tool is used by your monitoring agent to check on active render tasks. The agent polls the status and reports back to the group when the video is ready. If the render fails or times out, the agent can trigger a retry or alert the team. This ensures your automated workflows don't get stuck waiting on a single render.

Setup guide

Set up Fliki (AI Text-to-Video & Speech API) 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 Fliki (AI Text-to-Video & Speech API) 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="Fliki (AI Text-to-Video & Speech API)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Fliki (AI Text-to-Video & Speech API) 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="Fliki (AI Text-to-Video & Speech API)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Fliki (AI Text-to-Video & Speech API) 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 Fliki. 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.

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Common questions about Fliki (AI Text-to-Video & Speech API) MCP in AutoGen

One agent drafts the script, another runs `list_voices` to pick the voice, and a third agent executes `generate_video` to render the final asset.

Yes. By exposing this MCP Server to your assistant agents, they can call these tools during their conversation to generate and check video renders.

The agent uses the `get_video` tool with the job ID returned by the generator. It can run this check periodically until the status changes to completed.

Your monitoring agent will detect the failure via `get_video` and can ask the writer agent to adjust the script parameters before retrying the generation.

All authentication tokens are handled securely at the transport layer. Vinkius runs this MCP Server in a zero-trust, ephemeral sandbox, so your keys and video configurations remain secure.

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