How to Use the Transloadit MCP in CrewAI

Q: How do I make Transloadit assemblies run automatically with CrewAI?

You define the sequence using createassembly. Your autonomous crew simply calls this tool, and the MCP Server handles the entire complex workflow of encoding videos or resizing images.

Q: Can I track cost metrics for my multi-agent operations with Transloadit and CrewAI?

Yes. Use getbillingusage. You pass the month in YYYY-MM format, and the server returns a clear record of your file processing usage and costs.

Q: If an agent needs to adjust the workflow steps, how do I handle that with Transloadit and CrewAI?

First, review the current setup using gettemplatedetails. Then, save the corrected logic via createprocessingtemplate, ensuring all agents use the updated, correct template.

Q: What happens if an assembly is stuck? Transloadit and CrewAI can help?

If a job hangs, call cancelassembly to force an immediate stop. You then have options like listing recent jobs with listassemblies before deciding whether to re-run it with replayassembly.

Build autonomous media pipelines with CrewAI using Transloadit's MCP Server for specialized agents.

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Connect Transloadit MCP to CrewAI

Create your Vinkius account to connect Transloadit to CrewAI 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 Transloadit with CrewAI

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Perplexity

Coordinating Multi-Step Pipelines

Have Agent A prepare the files, and Agent B encode them? Use `create_assembly` to define that entire pipeline flow. The CrewAI agent simply executes this single tool call to manage the full media processing sequence.

Monitoring Pipeline Health

A monitor agent needs visibility into running jobs. Call `list_assemblies` and `get_assembly_details`. This allows a specialized agent to check the status of several Transloadit assemblies across different stages.

Defining Shared Media Assets

Don't let agents guess at file formats. Use `create_processing_template` to define exactly what kind of media processing is allowed. This template name acts as the shared memory for all specialized crew members.

Setup guide

Set up Transloadit MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Transloadit tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Transloadit Analyst",
    goal="Access and analyze Transloadit data via MCP.",
    backstory="Expert analyst with direct Transloadit access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Transloadit transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Transloadit Analyst",
        goal="Access and analyze Transloadit data via MCP.",
        backstory="Expert analyst with direct Transloadit access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Transloadit transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Transloadit. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Transloadit MCP in CrewAI

You define the sequence using `create_assembly`. Your autonomous crew simply calls this tool, and the MCP Server handles the entire complex workflow of encoding videos or resizing images.

Yes. Use `get_billing_usage`. You pass the month in YYYY-MM format, and the server returns a clear record of your file processing usage and costs.

First, review the current setup using `get_template_details`. Then, save the corrected logic via `create_processing_template`, ensuring all agents use the updated, correct template.

If a job hangs, call `cancel_assembly` to force an immediate stop. You then have options like listing recent jobs with `list_assemblies` before deciding whether to re-run it with `replay_assembly`.

The server accesses file processing usage and costs. You pass a month in YYYY-MM to `get_billing_usage`, which returns the necessary financial breakdown.

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