Can I see how many likes and views a video has through the agent?

Yes. The `get_video_details` tool allows your AI agent to retrieve full real-time statistics for any YouTube video ID, including view counts, like counts, and the total number of comments.

How do I find out the subscriber count for a specific channel?

You can use the `get_channel_details` tool. Provide the unique channel ID, and your agent will return the channel's performance statistics, including subscriber counts, total views, and video counts.

Is it possible to read the comments on a video via chat?

Absolutely. Use the `list_video_comments` tool to retrieve the top most relevant or recent comment threads from any video ID, helping you perform rapid sentiment analysis through conversation.

YouTube MCP Server for CrewAI 4 tools — connect in under 2 minutes

Name: YouTube
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 4 Tools Framework

Connect your CrewAI agents to YouTube through Vinkius, pass the Edge URL in the `mcps` parameter and every YouTube tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="YouTube Specialist",
    goal="Help users interact with YouTube effectively",
    backstory=(
        "You are an expert at leveraging YouTube tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in YouTube "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 4 available tools "
        "and what they can do."
    ),
)

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

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About YouTube MCP Server

Connect your YouTube Data API account to any AI agent and harness the power of global video intelligence through natural conversation.

When paired with CrewAI, YouTube becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call YouTube tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Universal Search — Find relevant video content by keyword or exact phrase, retrieving a list of metadata including titles and descriptions
Deep Video Insights — Retrieve full technical metadata for specific videos, including view counts, like counts, and engagement statistics
Channel Performance — Monitor any YouTube channel's branding and statistics, including total subscriber counts and video volume
Sentiment Analysis — Fetch the most relevant comments from any video to analyze user feedback and community engagement
Content Discovery — Quickly find unique video and channel IDs required for automated media monitoring workflows
Trend Auditing — Browse and analyze video descriptions and statistics to identify content patterns and audience interests
Metadata Retrieval — Get high-resolution thumbnails and precise upload timestamps for any piece of video content

The YouTube MCP Server exposes 4 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect YouTube to CrewAI via MCP

Follow these steps to integrate the YouTube MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 4 tools from YouTube

Why Use CrewAI with the YouTube MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with YouTube through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

YouTube + CrewAI Use Cases

Practical scenarios where CrewAI combined with the YouTube MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries YouTube for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries YouTube, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain YouTube tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries YouTube against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

YouTube MCP Tools for CrewAI (4)

These 4 tools become available when you connect YouTube to CrewAI via MCP:

get_channel

Retrieves complete statistics and branding information for a YouTube channel

get_video

Retrieves full metadata, description, and statistics for a specific YouTube video

list_comments

Returns the most recent/relevant comment threads. Fetches the top most relevant comments from a specific YouTube video

search_videos

Returns a list of video metadata including titles and descriptions. Search for YouTube videos by keyword or exact phrase

Example Prompts for YouTube in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with YouTube immediately.

"Search YouTube for 'generative AI tutorials' and show me the top 5 results."

"What are the statistics for video ID 'dQw4w9WgXcQ'?"

"Check the subscriber count for channel ID 'UC_x5XG1OV2P6uYZ5M1D2ogw'."

Troubleshooting YouTube MCP Server with CrewAI

Common issues when connecting YouTube to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

YouTube + CrewAI FAQ

Common questions about integrating YouTube MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.