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 CrewAI 11 tools — connect in under 2 minutes

Name: Mux
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 11 Tools Framework

Connect your CrewAI agents to Mux through Vinkius, pass the Edge URL in the `mcps` parameter and every Mux tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Mux Specialist",
    goal="Help users interact with Mux effectively",
    backstory=(
        "You are an expert at leveraging Mux tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Mux "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 11 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, Mux becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Mux tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the Mux MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 11 tools from Mux

Why Use CrewAI with the Mux MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Mux through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Mux + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Mux for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Mux, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Mux tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Mux against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Mux MCP Tools for CrewAI (11)

These 11 tools become available when you connect Mux to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Mux + CrewAI FAQ

Common questions about integrating Mux MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.