YouTube MCP Server for LlamaIndex 4 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add YouTube as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to YouTube. "
"You have 4 tools available."
),
)
response = await agent.run(
"What tools are available in YouTube?"
)
print(response)
asyncio.run(main())
* 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.
LlamaIndex agents combine YouTube tool responses with indexed documents for comprehensive, grounded answers. Connect 4 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the YouTube MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 4 tools from YouTube
Why Use LlamaIndex with the YouTube MCP Server
LlamaIndex provides unique advantages when paired with YouTube through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine YouTube tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain YouTube tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query YouTube, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what YouTube tools were called, what data was returned, and how it influenced the final answer
YouTube + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the YouTube MCP Server delivers measurable value.
Hybrid search: combine YouTube real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query YouTube to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying YouTube for fresh data
Analytical workflows: chain YouTube queries with LlamaIndex's data connectors to build multi-source analytical reports
YouTube MCP Tools for LlamaIndex (4)
These 4 tools become available when you connect YouTube to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting YouTube to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpYouTube + LlamaIndex FAQ
Common questions about integrating YouTube MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect YouTube with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
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TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect YouTube to LlamaIndex
Get your token, paste the configuration, and start using 4 tools in under 2 minutes. No API key management needed.