How to Use the Verbit MCP in OpenAI Agents SDK

Q: Can I track progress with Verbit via OpenAI Agents SDK?

Yes. You use getjob to poll the status. Your agent checks this tool repeatedly until the job status changes from 'processing' to 'complete', confirming readiness for download.

Automate professional media transcription and captioning with OpenAI Agents SDK.

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Connect Verbit MCP to OpenAI Agents SDK

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

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Setup Verbit with OpenAI Agents SDK

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Manage Transcription Jobs

The `create_job` tool lets you upload any media file. Your agent sends the file to Verbit, which starts the processing job immediately. Once the job is running, use `get_job` to check its status. This tells your AI client exactly when the transcription is ready for download.

Retrieve Completed Transcripts

Verbit provides multiple formats for transcripts. When the job finishes, call `get_transcript` to pull the finished data. This ensures your agent can process and use the captioning information right away, whether you're sending it into another system or logging it.

Orchestrate Full Media Processing

Your agent coordinates the entire workflow using all three tools. First, `create_job` ingests your media file. Then, the agent loops through `get_job` until status is 'complete'. Finally, it uses `get_transcript` to download the usable transcript data for downstream steps.

Setup guide

Set up Verbit MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Verbit tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Verbit tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Verbit tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Verbit Agent",
            instructions="You have access to Verbit tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Verbit MCP in OpenAI Agents SDK

The OpenAPI Agent SDK auto-discovers the Verbit tools without extra setup. Your agent treats it like any other built-in capability, letting you call functions like `create_job` directly.

Verbit handles raw media files and generates structured transcript text. The agent's job is managing the lifecycle of that uploaded content, from upload to final download.

Yes. You use `get_job` to poll the status. Your agent checks this tool repeatedly until the job status changes from 'processing' to 'complete', confirming readiness for download.

The process is contained within the MCP Server framework. When your agent calls `get_job`, it only receives status updates, ensuring you know exactly where the media file is in the processing queue.

Verbit handles uploaded media files and resulting text transcript data. Your agent uses this data to build out final records or messages.

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