How to Use the YouTube MCP in CrewAI

Q: How can CrewAI research YouTube content ideas?

The 'Researcher' agent executes searchvideos. It returns a list of titles and descriptions, which the crew then uses to plan out new content strategies autonomously.

Q: What kind of statistics does the MCP Server provide for YouTube channels used with CrewAI?

The server provides stats via getchannel. The 'Auditor' agent collects this performance data, which is then stored in the crew's shared memory for later analysis.

Q: Does CrewAI handle comment threads for YouTube videos?

Yes. The 'Analyst' agent uses listcomments to pull relevant threads associated with a specific video, giving you raw audience feedback that the crew can then process.

Q: Can I get detailed stats for a specific YouTube video with CrewAI?

You use the getvideo tool. This pulls all metadata and performance statistics needed, allowing your specialized agents to build a complete report without human intervention.

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Connect YouTube MCP to CrewAI

Create your Vinkius account to connect YouTube 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 YouTube with CrewAI

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Autonomous Market Research

The 'Researcher' agent uses the `search_videos` tool to scan for new keywords across YouTube. This initial sweep returns a list of video metadata, giving your crew actionable content leads. The shared memory ensures that every subsequent agent (like the Analyst) knows exactly what search criteria were used and which results came back.

Performance Auditing with CrewAI

The 'Auditor' agent runs `get_channel` to benchmark a target YouTube channel. It pulls all the core statistics needed for a performance report. This process is highly specialized: one agent monitors the overall health, while another processes the raw data points from the MCP Server.

In-Depth Content Analysis

The 'Analyst' agent specializes in deep reviews. It uses `get_video` for the core stats and metadata, then calls `list_comments`. The crew handles both tools together. This allows your autonomous operation to build a complete picture: video performance combined with audience sentiment.

Setup guide

Set up YouTube 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 YouTube tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent YouTube 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="YouTube Analyst",
        goal="Access and analyze YouTube data via MCP.",
        backstory="Expert analyst with direct YouTube access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent YouTube 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 YouTube. 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 YouTube MCP in CrewAI

The 'Researcher' agent executes `search_videos`. It returns a list of titles and descriptions, which the crew then uses to plan out new content strategies autonomously.

The server provides stats via `get_channel`. The 'Auditor' agent collects this performance data, which is then stored in the crew's shared memory for later analysis.

Yes. The 'Analyst' agent uses `list_comments` to pull relevant threads associated with a specific video, giving you raw audience feedback that the crew can then process.

The server accesses public statistics, metadata, and comment threads. CrewAI ingests these three types of structured data to run its multi-agent operations.

You use the `get_video` tool. This pulls all metadata and performance statistics needed, allowing your specialized agents to build a complete report without human intervention.

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