Vidyard MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Vidyard as an MCP tool provider through 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 Vidyard. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Vidyard?"
)
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 Vidyard MCP Server
Connect your Vidyard account to any AI agent and manage your business video library through natural conversation.
LlamaIndex agents combine Vidyard tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through 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
- Video Library — List all video assets stored in your Vidyard dashboard and retrieve unique video IDs
- Metadata & Details — Get technical metadata for any video including length, encoding status, and descriptions
- Player Management — Create, update, and manage video players (facades) to customize how your content is embedded
- Video Organization — Attach videos to specific players and organize your content for different marketing or sales needs
- Direct Stream Access — Retrieve direct stream URLs and raw MP4 download links for various video qualities (480p, 720p, etc.)
- Asset Management — Rename players or permanently delete video assets and player containers from your dashboard
The Vidyard MCP Server exposes 10 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 Vidyard to LlamaIndex via MCP
Follow these steps to integrate the Vidyard 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 10 tools from Vidyard
Why Use LlamaIndex with the Vidyard MCP Server
LlamaIndex provides unique advantages when paired with Vidyard through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Vidyard tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Vidyard tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Vidyard, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Vidyard tools were called, what data was returned, and how it influenced the final answer
Vidyard + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Vidyard MCP Server delivers measurable value.
Hybrid search: combine Vidyard real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Vidyard 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 Vidyard for fresh data
Analytical workflows: chain Vidyard queries with LlamaIndex's data connectors to build multi-source analytical reports
Vidyard MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Vidyard to LlamaIndex via MCP:
attach_video_to_player
Requires both player ID and video ID. Adds a video asset into a specific player container
create_empty_player
Creates a new, empty video player container
delete_video_asset
This action is irreversible. Permanently deletes a video asset from Vidyard
delete_video_player
Note that the original video assets are not deleted. Permanently deletes a video player
get_player_details
Retrieves details for a specific video player
get_video_details
Retrieves technical metadata for a specific video asset
get_video_source_files
Retrieves direct stream URLs for various video qualities (480p, 720p, etc.)
list_video_players
Lists all configured video players (facades) in the account
list_videos
Lists all video assets stored in the Vidyard dashboard
update_player_name
Updates the display name or title of an existing player
Example Prompts for Vidyard in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Vidyard immediately.
"List all my videos in Vidyard."
"Get the direct download links for the 'Product Demo' video."
"Create a new player called 'Website Homepage' and attach my latest video to it."
Troubleshooting Vidyard MCP Server with LlamaIndex
Common issues when connecting Vidyard to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpVidyard + LlamaIndex FAQ
Common questions about integrating Vidyard 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 Vidyard with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
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 Vidyard to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.