How to Use the Verbit MCP in Google ADK

Q: Can I use Verbit's tools in a long-context workflow with Google ADK?

Absolutely. The agent can call getjob and then pass the resulting transcript data into a massive context window, allowing for deep analysis across 1M+ tokens.

Process Media Transcriptions on Google Cloud with the Google ADK.

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Connect Verbit MCP to Google ADK

Create your Vinkius account to connect Verbit to Google ADK 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 Google ADK

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Upload and Initiate Transcription

Use `create_job` to upload a media file directly from your agent running on Google ADK. Verbit handles the heavy lifting of starting the professional transcription process. This allows you to initiate batch processing workflows entirely within your BigQuery-connected Google Cloud environment.

Monitor Job Status in GCP

Your agent calls `get_job` to check progress. This tool confirms if Verbit is currently transcribing the media file. This checkpointing allows your large-context Gemini models to wait reliably, preventing workflow stalls while waiting for external processing completion.

Get Final Transcripts into Vertex AI

After confirming job completion with `get_job`, the agent calls `get_transcript`. This tool downloads the finished transcript in a usable format. These structured transcripts are perfect inputs for subsequent reasoning steps within your enterprise agents.

Setup guide

Set up Verbit MCP in Google ADK

Prerequisites

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

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Verbit tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Verbit_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Verbit tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Verbit MCP in Google ADK

You pass the MCP toolset containing Verbit to your LlmAgent. The agent then auto-discovers and calls functions like `create_job` as part of its natural reasoning flow.

Verbit takes media files and returns structured transcript text. This is ideal for agents that need to analyze large volumes of content within BigQuery or Vertex AI.

Absolutely. The agent can call `get_job` and then pass the resulting transcript data into a massive context window, allowing for deep analysis across 1M+ tokens.

Yes. The agent framework supports robust streaming and HTTP transports when talking to the MCP Server, making it suitable for continuous enterprise deployment.

Verbit manages media files (inputs) and structured text transcripts (outputs). Your agent uses these results for analysis across Google Cloud services.

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