How do I create a Mux API Token?

Go to the Mux Dashboard, navigate to Settings > API Tokens, and click 'Create new token'. Ensure you copy the Token Secret immediately as it will only be shown once.

What format should the asset URL be?

The URL must be publicly accessible and point directly to a video file (e.g., MP4, MOV). Mux will then fetch and process the file from that location.

Is the analytics data real-time?

Yes, the `get_recent_views` tool retrieves near real-time data about video views occurring in your account.

Mux MCP Server for Pydantic AI 11 tools — connect in under 2 minutes

Name: Mux
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 11 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Mux through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Mux "
            "(11 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Mux?"
    )
    print(result.data)

asyncio.run(main())

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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 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.

Pydantic AI validates every Mux tool response against typed schemas, catching data inconsistencies at build time. Connect 11 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Mux MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

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 with type-safe schemas

Why Use Pydantic AI with the Mux MCP Server

Pydantic AI provides unique advantages when paired with Mux through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Mux integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Mux connection logic from agent behavior for testable, maintainable code

Mux + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Mux MCP Server delivers measurable value.

Type-safe data pipelines: query Mux with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Mux tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Mux and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Mux responses and write comprehensive agent tests

Mux MCP Tools for Pydantic AI (11)

These 11 tools become available when you connect Mux to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

Common issues when connecting Mux to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Mux + Pydantic AI FAQ

Common questions about integrating Mux MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your Mux MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.