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How to Use the Vimeo MCP in Google ADK

Act on Vimeo data with Google ADK and BigQuery integration.

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Google ADK

Connect Vimeo MCP to Google ADK

Create your Vinkius account to connect Vimeo 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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Audit Organizational Content

You can run `list_channels` to see all the channels you follow on Vimeo. The agent also uses `list_groups` to map out your associated groups. This ability to list multiple content types—folders, showcases, and videos—allows your Google ADK system to build a holistic inventory of your entire profile.

Retrieve Specific Video Details

The `get_video` tool gives you full details for any specific Vimeo video by its ID. This rich data includes metadata, view counts, and usage rights. If you only need a list of videos associated with your user account, the `list_videos` tool provides that core inventory.

Manage Content Lifecycle

The system handles content changes using `update_video`, allowing you to change titles or descriptions without manual intervention. It can also execute `delete_video` when a video must be removed. Because this uses Google ADK, the results of these actions are immediately available for long-context reasoning in BigQuery.

Setup guide

Set up Vimeo 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. 1

    Install Google ADK

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

  2. 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. 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. 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 Vimeo 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="Vimeo_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Vimeo tools via MCP.",
    tools=mcp_tools,
)

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

The agent calls `search_videos` and passes the necessary query parameters. The result is a list of public video matches on Vimeo, which you can then feed directly into Vertex AI for further analysis.
Yes. You use the `delete_video` tool within your agent workflow. Since this is running on Google Cloud, you'll have full traceability of the deletion event for compliance.
The `list_groups` tool fetches all the associated groups from your account. This data is streamed to Gemini models, allowing you to analyze group patterns against other BigQuery datasets.
The agent executes `update_video` and passes the new details. Since your workflow is built on GCP, you get immediate confirmation of success or failure, maintaining data integrity.
This MCP Server touches video metadata and user profile information via `get_me`. Your agent processes this to ensure that all actions performed on Vimeo are logged accurately in BigQuery, maintaining a clear audit trail.

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