How to Use the Tubular MCP in CrewAI

Q: How do I find viewer demographics using Tubular and CrewAI?

You use getvideodemographics. Your specialized agent calls this tool for the target video. The resulting data is then fed into a separate analysis agent to interpret the meaning of the viewer base.

Q: Can the crew analyze sponsorship opportunities with Tubular and CrewAI?

Yes. The team can execute listsponsors to gather all available brand partners. A specialized agent then takes that list and cross-references it against current video trends for actionable recommendations.

Q: Can the MCP Server track creator growth with Tubular and CrewAI?

Absolutely. You can use getcreatortrends to see a creator's historical trajectory, giving your team a clear picture of their long-term growth pattern.

Run full competitive analysis teams with CrewAI powered by Tubular's MCP Server.

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Connect Tubular MCP to CrewAI

Create your Vinkius account to connect Tubular to CrewAI 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 CrewAI

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Multi-Agent Competitive Analysis for CrewAI

You don't run one query; you run a team. Assign Agent A to research potential competitors using `search_creators`. Then, pass those results to Agent B, which analyzes their performance via `get_creator_summary`. The shared memory feature lets the whole crew work off the same data set, making complex competitive benchmarking straightforward and repeatable.

Autonomous Campaign Monitoring with CrewAI

Set up a monitoring agent to watch for brand presence. It calls `list_sponsored_campaigns` to gather all current campaigns. A moderator agent can then use that list, combined with `get_video_trends`, to flag any sudden shifts in market attention. This is autonomous operations: the crew watches the data and flags actionable insights without needing human intervention.

Deep Dive into Audience Performance using CrewAI

Give your agents deep analytical roles. One agent gets video demographics via `get_video_demographics`, while another uses `get_audience_overlap` to measure shared audience potential between the videos analyzed. The crew can then synthesize these findings—the 'who' and the 'where'—into a single, comprehensive report automatically.

Setup guide

Set up Tubular MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Tubular tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Tubular Analyst",
    goal="Access and analyze Tubular data via MCP.",
    backstory="Expert analyst with direct Tubular access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Tubular transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

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

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Tubular Analyst",
        goal="Access and analyze Tubular data via MCP.",
        backstory="Expert analyst with direct Tubular access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Tubular transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

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 CrewAI

You use `get_video_demographics`. Your specialized agent calls this tool for the target video. The resulting data is then fed into a separate analysis agent to interpret the meaning of the viewer base.

The MCP Server handles public social metrics and creator metadata, including audience demographics. It never accesses private user account passwords or financial records.

Yes. The team can execute `list_sponsors` to gather all available brand partners. A specialized agent then takes that list and cross-references it against current video trends for actionable recommendations.

Absolutely. You can use `get_creator_trends` to see a creator's historical trajectory, giving your team a clear picture of their long-term growth pattern.

The server performs audience overlap analysis between entities using shared viewing pools. This helps you understand the potential size of a combined market by analyzing common viewer demographics.

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