How to Use the Elai AI Video MCP in CrewAI

Q: How can one CrewAI agent create a video with Elai AI Video and another check its status?

You'll use CrewAI's shared context. The first agent calls createnewaivideo and writes the returned video ID to the context. The second agent in the sequence can then read that ID from the context and use it to call getvideodetails through the same MCP server connection.

Deploy a crew of AI agents to manage your Elai AI Video production, from script to final render, with CrewAI.

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Connect Elai AI Video MCP to CrewAI

Create your Vinkius account to connect Elai AI Video 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 Elai AI Video with CrewAI

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Assign Video Production Roles to Agents

Break down video production into specialized jobs for your AI crew. You can create a 'Producer' agent that takes a high-level goal, uses `list_video_templates` to find a good starting point, and picks an actor from `list_available_avatars`. Then, the Producer tasks a 'Director' agent to take that plan, along with a script, and execute the `create_new_ai_video` call. CrewAI manages the handoff between agents, letting each one focus on its specific part of the job.

Autonomous Video Monitoring & Response

Build a crew that watches over your video pipeline 24/7. Assign a 'Monitor' agent the task of periodically calling `list_ai_videos` and then `get_video_details` for any project that's currently rendering. It stores the status of all active jobs in the crew's shared memory. If the Monitor agent detects a video that has failed or is stuck, it delegates a task to an 'Operator' agent. The Operator can try re-rendering the project with `trigger_video_rendering` or, if that fails, escalate the issue to a human. This is fully autonomous operations, driven by your crew and the Elai MCP server.

Scale Content Localization with a CrewAI Team

Set up a localization pipeline with a dedicated crew. The first agent in the crew takes an English video script. A second, specialized 'Translator' agent converts the script into five different languages. A 'Casting' agent then takes those translated scripts, finds appropriate voices for each language using `list_available_voices`, and passes the paired script-and-voice data to a 'Production' agent. The final agent calls `create_new_ai_video` for each language, running all five jobs in parallel. This entire complex workflow is managed by your CrewAI team and this MCP server.

Setup guide

Set up Elai AI Video 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 Elai AI Video tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Elai AI Video 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="Elai AI Video Analyst",
        goal="Access and analyze Elai AI Video data via MCP.",
        backstory="Expert analyst with direct Elai AI Video access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Elai AI Video 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 Elai AI Video. 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 Elai AI Video MCP in CrewAI

You'll use CrewAI's shared context. The first agent calls `create_new_ai_video` and writes the returned video ID to the context. The second agent in the sequence can then read that ID from the context and use it to call `get_video_details` through the same MCP server connection.

Yes. When you configure the tools for an agent, you can provide a specific list. For more advanced control, CrewAI's `MCPServerHTTP` class from `crewai.mcp` lets you use a `tool_filter` to expose only a subset of the server's tools to a particular agent.

Use a hierarchical crew. A 'Manager' agent oversees the whole process. It delegates tasks to a 'Creative' agent (picks templates/avatars), a 'Production' agent (calls `create_new_ai_video`), and a 'QA' agent (checks status with `get_video_details` and lists finished videos).

For a simple setup, just pass the Vinkius URL for the Elai AI Video MCP server into the `mcps` list when you instantiate your Agent. CrewAI will automatically fetch the tools and make them available.

Your agents will send data like video scripts, template names, and avatar IDs to the Elai AI Video server to execute tool calls. This data is handled within the Vinkius zero-trust sandbox, which isolates each request. The data is not stored by Vinkius.

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