How to Use the Huddle01 (Web3 Video API) MCP in AutoGen

Q: Can AutoGen agents negotiate room settings using Huddle01 (Web3 Video API)?

Yes, one agent can propose a locked room using createroom, and another agent can verify the metadata via getroommetadata. They negotiate and update the room setup before inviting users.

Q: How do agents handle active caller metrics in AutoGen?

Agents query getmetrics and getlivesessions to evaluate overall platform usage. They debate resource allocation and decide if they need to spin down idle rooms to save costs.

Coordinate Web3 video rooms and monitor active sessions through multi-agent debate in AutoGen.

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Connect Huddle01 (Web3 Video API) MCP to AutoGen

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Setup Huddle01 (Web3 Video API) with AutoGen

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Coordinate room creation via multi-agent debate

The `create_room` tool allows your AutoGen agents to provision new Huddle01 meetings programmatically. A scheduling agent initiates the request, while a security agent reviews the lock settings before executing the call. This collaborative decision-making process ensures that rooms are only created when all agents agree on the configuration. The resulting room ID is then shared across the agent group for immediate deployment.

Resolve active session conflicts with AutoGen

The `get_live_sessions` tool retrieves all active video rooms for your API key. In AutoGen, a monitoring agent can flag an over-capacity room, prompting a coordination agent to check details using `get_live_session_details`. The agents debate whether to spin up a secondary room or restrict access. They use `get_live_session_participants` to identify who to migrate, resolving capacity issues without human intervention.

Audit meeting recordings using an MCP Server

The `get_recordings` tool pulls completed meeting files into the AutoGen conversation. A compliance agent analyzes the session history via `get_room_sessions`, while a storage agent tracks API usage with `get_metrics`. This MCP Server setup enables a complete audit loop where agents verify that every recorded call matches the expected participant list from `get_participant_list`. The agents reach consensus before archiving the files.

Setup guide

Set up Huddle01 (Web3 Video API) 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 Huddle01 (Web3 Video API) 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="Huddle01 (Web3 Video API)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Huddle01 (Web3 Video API) data")
print(result.messages[-1].content)

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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="Huddle01 (Web3 Video API)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Huddle01 (Web3 Video API) data")
    print(result.messages[-1].content)

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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 Huddle01. 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 Huddle01 (Web3 Video API) MCP in AutoGen

You register the MCP tools with your AssistantAgent using the AutoGen MCP adapter. Agents can then call tools like `create_room` or `get_rooms` during their multi-turn conversations.

Yes, one agent can propose a locked room using `create_room`, and another agent can verify the metadata via `get_room_metadata`. They negotiate and update the room setup before inviting users.

Agents query `get_metrics` and `get_live_sessions` to evaluate overall platform usage. They debate resource allocation and decide if they need to spin down idle rooms to save costs.

Yes, the AutoGen MCP adapter handles schema conversion for all 11 tools over stdio or HTTP. This allows your agents to call `get_participant_list` or `get_room_details` without manual parsing.

Vinkius runs the MCP Server in an isolated, zero-trust environment, protecting your API keys. Participant lists and session details are only exposed to the specific AutoGen agents authorized to call `get_participant_list`.

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