How to Use the Vidyard MCP in AutoGen

Q: How can AutoGen mediate decisions about Vidyard players?

Agents negotiate outcomes by reading player data via getplayerdetails. They debate if the current state is optimal and then converge on a decision, which might involve calling updateplayername.

Q: What if I need to create new Vidyard containers with AutoGen?

One agent can propose creating a new container using createemptyplayer. Other agents then debate the naming conventions or initial settings before the system confirms the player exists.

Drive consensus decisions on Vidyard assets using AutoGen's multi-agent debate framework.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Vidyard MCP to AutoGen

Create your Vinkius account to connect Vidyard to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Vidyard with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Debate player name changes.

Agent A can check the current state with `get_player_details`. Agent B proposes a change, which is then executed by calling `update_player_name`. The agents debate if the new name makes sense given the existing context. This conversation structure forces deliberation before making an operational change to the Vidyard asset.

Simulate player lifecycle consensus.

You can model a decision process by first creating a new placeholder container using `create_empty_player`. Agents then debate whether this player needs to be deleted later, running the logic through `delete_video_player`. This simulates system governance: one agent proposes creation, and another argues for its eventual removal.

Manage asset disposal decisions.

When a video is obsolete, agents can debate whether it should be permanently removed. The process involves checking the asset via `get_video_details`, followed by an irreversible decision to call `delete_video_asset`. This multi-agent approach ensures that deletion isn't just a single command; it’s a consensus outcome.

Setup guide

Set up Vidyard MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Vidyard tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Vidyard_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Vidyard data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Vidyard_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Vidyard data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Vidyard. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Vidyard now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Vidyard MCP in AutoGen

Agents negotiate outcomes by reading player data via `get_player_details`. They debate if the current state is optimal and then converge on a decision, which might involve calling `update_player_name`.

Yes. You can set up a multi-agent debate where one agent recommends cleanup and another executes the irreversible action using tools like `delete_video_asset`. This simulates controlled, decision-driven deletion.

One agent can propose creating a new container using `create_empty_player`. Other agents then debate the naming conventions or initial settings before the system confirms the player exists.

You have agents check asset details first. They can validate the need for disposal, and if consensus is reached, they execute `delete_video_asset`. This prevents accidental loss of data.

The server touches video asset IDs and player container metadata. The agents' debate process uses this identifying information to reach a consensus, ensuring the right context is available for any action.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript