How to Use the api.video MCP in Google ADK

Q: How do I initialize api.video inside Google ADK?

Use the McpToolset class with StreamableHttpServerParameters. Point the URL parameter to your Vinkius MCP Server endpoint. Pass the resulting toolset into your LlmAgent initialization.

Q: Can Google ADK filter which api.video tools are exposed?

You can pass a toolnames list when configuring the toolset. This prevents the agent from seeing destructive tools like deletevideo if you only want it running analytics queries.

Q: Does api.video work with Gemini's long context window?

The agent can pull thousands of records using listvideos and feed them directly into Gemini. The 1M+ token limit means the model can reason over your entire video catalog's metadata at once.

Q: How do I get api.video viewer metrics into BigQuery using Google ADK?

Your agent calls getvideoanalytics via the MCP Server. Since it runs inside the Gemini ecosystem, it natively formats that JSON response and inserts it directly into your BigQuery tables.

Connect api.video to your Google ADK enterprise agents to manage encoded media alongside your BigQuery data via our MCP Server.

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Connect api.video MCP to Google ADK

Create your Vinkius account to connect api.video 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 api.video with Google ADK

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Google ADK Video Pipeline Automation

The `create_video_object` tool acts as the entry point for your Gemini agents to initialize new encoding jobs. Enterprise teams often store raw video metadata in BigQuery. Your agent reads that table, generates titles, and pushes the initialized records to the hosting platform. After initialization, `update_video_details` modifies the tags based on Gemini's long-context analysis of the video transcript. You pass the Vinkius MCP Server URL into the `McpToolset` class, and your agent immediately understands the required parameters.

Analyze Metrics with Vertex AI

`get_video_analytics` feeds raw playback metrics straight into Gemini's massive context window. An agent pulls the last year of viewer data for hundreds of videos using `list_videos` and cross-references those numbers with Vertex AI models. You don't need to build custom HTTP clients. The `McpToolset` handles the transport layer natively. The agent looks at the analytics, spots viewer drop-off points, and suggests new chapter markers.

Enforce Compliance Rules

`list_video_chapters` and `list_video_captions` let your agent read the exact timing metadata attached to your media. If your enterprise compliance rules require specific caption languages, the agent audits the entire library. It uses `get_video_details` to check the status of individual files. You can restrict which operations the agent performs by applying a `tool_names` filter during setup, making sure it only reads data instead of accidentally calling `delete_video`.

Setup guide

Set up api.video 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 api.video 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="api.video_agent",
    model="gemini-2.0-flash",
    instruction="You have access to api.video tools via MCP.",
    tools=mcp_tools,
)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by api.video. 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 api.video MCP in Google ADK

Use the `McpToolset` class with `StreamableHttpServerParameters`. Point the URL parameter to your Vinkius MCP Server endpoint. Pass the resulting toolset into your `LlmAgent` initialization.

You can pass a `tool_names` list when configuring the toolset. This prevents the agent from seeing destructive tools like `delete_video` if you only want it running analytics queries.

The agent can pull thousands of records using `list_videos` and feed them directly into Gemini. The 1M+ token limit means the model can reason over your entire video catalog's metadata at once.

Your agent calls `get_video_analytics` via the MCP Server. Since it runs inside the Gemini ecosystem, it natively formats that JSON response and inserts it directly into your BigQuery tables.

The tools access player themes, video titles, and viewer statistics. The Vinkius architecture relies on ephemeral execution. The connection drops immediately after the tool call finishes, leaving zero residual data on the gateway.

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