How to Use the Mux MCP in LlamaIndex

Turn your Mux video library into a searchable LlamaIndex knowledge base.

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Connect Mux MCP to LlamaIndex

Create your Vinkius account to connect Mux to LlamaIndex 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 Mux with LlamaIndex

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Vectorize Mux MCP Server Data

Your agent runs `list_assets` through the Mux MCP Server and embeds the output, turning raw JSON responses into a semantic index. The server feeds your video metadata straight into LlamaIndex. Users query the index to find specific playback IDs or cross-reference upload dates. The agent grounds its answers in actual API data, eliminating hallucinations.

Query Playback Metrics

By executing `get_recent_views`, your agent pulls raw playback numbers and indexes them alongside your asset tags. Video analytics usually live in isolated dashboards, but this brings them into your searchable knowledge base. The agent retrieves your stored data from `list_live_streams` when you ask which broadcasts performed best. It correlates the view counts and outputs a factual summary based entirely on your account history.

Track Active Uploads

Calling `list_direct_uploads` fetches the status of every pending video file. Managing user-generated content requires strict tracking, and this tool provides the exact state of your ingest queue. Your system searches the indexed upload sessions to see if a user's video stalled. It can even trigger `get_asset` to verify if the encoding failed on the backend.

Setup guide

Set up Mux MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Mux MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Mux tools.",
)
response = await agent.run("List recent Mux data")

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Common questions about Mux MCP in LlamaIndex

Set up a basic client and wrap it with the tool spec. Call the async tool list method and pass the array to your function agent.

Yes. The agent pulls your broadcast history and indexes the metadata. You can then run semantic queries against your past events.

It can, provided you include the delete tool in your allowed filters. You control exactly which operations the agent has permission to execute.

The framework respects standard HTTP responses from the MCP endpoint. If you hit a limit, the agent receives the error and can retry or halt.

The MCP protocol establishes a direct line between the Vinkius-hosted server and your vector store. No third party intercepts your media metadata.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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