How to Use the Verbit MCP in AutoGen

Q: How does AutoGen manage Verbit's media processing workflow?

The agents debate the steps: they decide when to use createjob, how often to poll with getjob, and finally, who executes gettranscript.

Run consensus-driven media workflows with AutoGen.

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Setup Verbit with AutoGen

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AutoGen: Multi-Agent Debate Over Media Tasks

Multiple agents can discuss the optimal workflow for Verbit. One agent might suggest calling `create_job` to upload a media file, while another debates whether waiting for status checks (`get_job`) is necessary. The consensus mechanism forces deliberation on the sequence of API calls before execution.

MCP Server: Consensus-Driven Job Management

The `get_transcript` tool can be debated. A 'Security Agent' might challenge the download process, while a 'Performance Agent' pushes for immediate retrieval once status is confirmed via `get_job`. This simulates real-world decision-making where the answer isn't obvious.

AutoGen: Orchestrating File Upload and Retrieval

Agents can automatically pass outputs to each other. For instance, Agent A calls `create_job` (uploading the media file), passing the resulting Job ID directly to Agent B, which then uses that ID with `get_job`. The conversation structure dictates the entire process from upload to final download.

Setup guide

Set up Verbit 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 Verbit 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="Verbit_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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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="Verbit_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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Common questions about Verbit MCP in AutoGen

The agents debate the steps: they decide when to use `create_job`, how often to poll with `get_job`, and finally, who executes `get_transcript`.

Yes. The agents treat the media upload as a critical step in their debate, ensuring that the required inputs for subsequent tools are handled correctly.

Verbit deals with your raw media files and the resulting transcript text. The multi-agent system ensures that access to this sensitive content is mediated through defined conversational roles.

Yes, the underlying transport supports streamable HTTP connections, which helps maintain flow when agents are discussing and executing multiple Verbit tool calls sequentially.

The core data handled is the media file itself, its job status records, and the final downloadable transcript content.

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