How to Use the Tubular MCP in AutoGen

Q: How does Tubular assist with AutoGen's multi-agent flow?

The MCP Server provides the data points—like getaudienceratings—that become the basis for agent debate. The agents argue over interpreting these metrics, leading to a much richer, vetted conclusion.

Q: Can AutoGen use Tubular's tools to list sponsors?

Yes, using listsponsors provides a definitive roster of brand partners. Different agents can then take turns cross-referencing those names with other available data for deeper analysis.

Q: What is the best way to check Tubular's availability with AutoGen?

The getapiratelimits tool allows your system to proactively check usage. This ensures that when agents start debating, they don't hit a wall and halt the entire consensus process.

Q: What if I need to see trending content with Tubular and AutoGen?

Use getvideotrends as one input source. The debate can then involve comparing those trends against historical performance data (e.g., checking a specific creator's long-term metrics).

Achieve consensus on social analytics with AutoGen's multi-agent debate framework.

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Connect Tubular MCP to AutoGen

Create your Vinkius account to connect Tubular 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.

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Setup Tubular with AutoGen

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Debating Campaign Strategy using MCP Server

When you list all campaigns, different agents can argue over the best direction. One agent might use `list_sponsored_campaigns` to establish a baseline of current activity. A second agent uses `get_audience_overlap` to challenge that baseline by pointing out untapped audience intersections. The system forces deliberation: 'Should we focus on Campaign X because it's big, or Campaign Y because the audience overlap is massive?' The MCP Server provides all the raw data for the agents to debate over.

Consensus-driven Creator Performance Review with AutoGen

An agent can be assigned to track general performance using `get_creator_summary`. Another specialized agent might use `get_video_insights` on a specific video. They debate which metrics matter most for success. The ultimate decision—the consensus answer—is based on synthesizing both the high-level summary and the granular, deep insights from the MCP Server.

Autonomous Market Research with AutoGen

You can set up a multi-agent system to research competitors. One agent calls `search_creators` using keywords while another uses `get_video_trends` to identify current market momentum. They argue over whether the focus should be on volume (many creators) or velocity (trending videos). This autonomous setup helps you reach a decision that no single tool call could provide.

Setup guide

Set up Tubular 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 Tubular 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="Tubular_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Tubular 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="Tubular_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Tubular 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 Tubular. 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 Tubular MCP in AutoGen

The MCP Server provides the data points—like `get_audience_ratings`—that become the basis for agent debate. The agents argue over interpreting these metrics, leading to a much richer, vetted conclusion.

Yes, using `list_sponsors` provides a definitive roster of brand partners. Different agents can then take turns cross-referencing those names with other available data for deeper analysis.

The `get_api_rate_limits` tool allows your system to proactively check usage. This ensures that when agents start debating, they don't hit a wall and halt the entire consensus process.

Use `get_video_trends` as one input source. The debate can then involve comparing those trends against historical performance data (e.g., checking a specific creator's long-term metrics).

This MCP Server works with aggregated audience demographics and video identifiers, specifically accessing demographic information (age ranges, genders) for the videos you analyze.

Use it with your favorite AI tools

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