How to Use the Vadoo AI MCP in Google ADK

Q: What data types can Vadoo AI handle for podcast generation with Google ADK?

The vadooaigeneratepodcast tool uses text-based scripts and prompts as input. The resulting job status is an auditable record that your agent can query via BigQuery.

Q: How do I check if a Vadoo AI job finished using Google ADK?

You use vadooaicheckgenerationstatus to monitor the lifecycle of the content. This function provides reliable state transitions that your agent can process, ensuring data consistency.

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Connect Vadoo AI MCP to Google ADK

Create your Vinkius account to connect Vadoo AI 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 Vadoo AI with Google ADK

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Generate AI videos from structured prompts.

When you call `vadooai_generate_video`, the agent treats the output job ID as a data point. You can then pass this reference to other Google Cloud functions for logging and auditing. This deep integration lets your enterprise workflow track video creation alongside data stored in BigQuery, giving full visibility into content provenance.

Process videos and extract captions or clips.

`vadooai_generate_caption` takes a finished video reference and returns structured text data. This JSON output can be immediately loaded into Vertex AI for further analysis. Similarly, `vadooai_create_clips` generates metadata about the short segments, allowing your agent to log key moments directly into Google Cloud storage.

Manage complex content status updates.

The tools are designed for state management. Use `vadooai_check_video_status` after initiating a job. This allows your Gemini-powered agent to pause and resume tasks based on external service readiness. This pattern is critical for reliable, long-running processes that involve multiple specialized services.

Setup guide

Set up Vadoo AI 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 Vadoo AI 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="Vadoo AI_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Vadoo AI tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Vadoo AI MCP in Google ADK

The `vadooai_generate_podcast` tool uses text-based scripts and prompts as input. The resulting job status is an auditable record that your agent can query via BigQuery.

Yes, the multi-step capability works smoothly. You can trigger `vadooai_generate_video` and then sequence calls to `vadooai_create_clips`, ensuring every asset is logged in your enterprise data lake.

You use `vadooai_check_generation_status` to monitor the lifecycle of the content. This function provides reliable state transitions that your agent can process, ensuring data consistency.

It’s ideal. Because it supports native BigQuery integration, you can build complex pipelines where video generation feeds directly into your existing enterprise data models.

This server touches video and text generation data. Because the agent is operating on Google Cloud, you benefit from native BigQuery logging and robust IAM controls over all inputs and outputs.

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