Mux MCP Server for LlamaIndex 11 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Mux as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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 Mux. "
"You have 11 tools available."
),
)
response = await agent.run(
"What tools are available in Mux?"
)
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 Mux MCP Server
Connect your Mux Video account to your AI agent and take full control of your video infrastructure and streaming workflows through natural conversation.
LlamaIndex agents combine Mux tool responses with indexed documents for comprehensive, grounded answers. Connect 11 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
- Asset Management — List all video assets and get real-time status updates, durations, and playback IDs.
- Asset Creation — Create new video assets instantly by providing a public URL to your source file.
- Live Streaming — Access and monitor your live stream configurations and their current status.
- Direct Uploads — Create secure upload sessions for uploading video files directly.
- Video Analytics — Get recent video view metrics to track your content's reach.
- Deep Inspection — Fetch complete metadata for specific assets or live streams using their unique IDs.
The Mux MCP Server exposes 11 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 Mux to LlamaIndex via MCP
Follow these steps to integrate the Mux 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 11 tools from Mux
Why Use LlamaIndex with the Mux MCP Server
LlamaIndex provides unique advantages when paired with Mux through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Mux tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Mux tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Mux, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Mux tools were called, what data was returned, and how it influenced the final answer
Mux + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Mux MCP Server delivers measurable value.
Hybrid search: combine Mux real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Mux 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 Mux for fresh data
Analytical workflows: chain Mux queries with LlamaIndex's data connectors to build multi-source analytical reports
Mux MCP Tools for LlamaIndex (11)
These 11 tools become available when you connect Mux to LlamaIndex via MCP:
create_asset
Create a new video asset
create_direct_upload
Create a direct upload session
create_live_stream
Create a new live stream
delete_asset
Delete a video asset
delete_live_stream
Delete a live stream
get_asset
Get specific asset details
get_live_stream
Get specific live stream details
get_recent_views
Get recent video views analytics
list_assets
List Mux video assets
list_direct_uploads
List direct video uploads
list_live_streams
List Mux live streams
Example Prompts for Mux in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Mux immediately.
"List all my video assets and show their status."
"Create a new video asset from https://example.com/video.mp4."
"Check recent views for my videos."
Troubleshooting Mux MCP Server with LlamaIndex
Common issues when connecting Mux to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpMux + LlamaIndex FAQ
Common questions about integrating Mux 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 Mux 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 Mux to LlamaIndex
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.
