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How to Use the Dailymotion MCP in Google ADK

Feed Dailymotion video metrics directly into your Gemini workflows using the Google ADK.

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Google ADK

Connect Dailymotion MCP to Google ADK

Create your Vinkius account to connect Dailymotion 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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Deep catalog ingestion with Gemini and Google ADK

The MCP Server tool `list_videos` extracts your entire Dailymotion catalog metadata including titles, durations, and view counts in a single pass. The agent feeds this raw dataset into Gemini's long-context window via the Google ADK to analyze performance trends over time. Because the Google ADK handles massive token limits, you can run complex reasoning over hundreds of Dailymotion videos at once. Gemini cross-references these details with Dailymotion channel structures fetched via `list_channels`.

Automated public content harvesting via MCP Server

The tool `search_videos` queries public Dailymotion uploads using specific keywords to find trending content in your niche. Your Gemini agent running on Google ADK parses the returned metadata to identify gaps in your own video library. The Google ADK lets you pipe these Dailymotion search results directly into BigQuery. From there, your agent can build automated reports on Dailymotion competitor tags and view counts without manual data entry.

Automated video lifecycle management

By exposing the MCP Server, `delete_video` removes outdated or underperforming content from your Dailymotion account based on strict performance thresholds. Your Google ADK agent checks performance metrics by querying `get_user` to verify your overall channel views. This automation prevents stale Dailymotion content from cluttering your profile. You define the rules in your Google ADK Python code, and the agent executes the deletion only when those specific conditions are met.

Setup guide

Set up Dailymotion 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 Dailymotion 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="Dailymotion_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Dailymotion 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 Dailymotion. 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 Dailymotion MCP in Google ADK

The SDK passes the parameters to `search_videos` and dumps the raw JSON payload into Gemini's context. The model's large context window easily processes hundreds of search results in a single turn.
Yes. You write a standard Python loop where the agent calls `get_user` or `list_videos` and writes the output dictionary straight into your BigQuery tables.
It does. The agent obtains a secure endpoint via `get_upload_url` and completes the registration with `publish_video` using tools exposed through the standard transport layer.
You configure the toolset filtering option during initialization. Simply omit `delete_video` from the exposed tool list so the agent physically cannot invoke that action.
Your API credentials and sensitive stream URLs retrieved by `get_video` reside in your secure Google Cloud environment or Vinkius vault. They are never transmitted to public LLM training sets.

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