How to Use the Videco MCP in CrewAI

Q: Which Videco tools are best for autonomous operations with CrewAI?

For full automation, you should expose both listleads and getvideoanalytics. This lets one agent research the leads while another analyzes their performance metrics automatically.

Q: What data types are handled by Videco for CrewAI?

The server manages Lead details. When setting up your crew, you can assign agents to specifically handle and process this lead information autonomously. It's crucial specialized knowledge for the team.

Q: Is the Videco MCP Server compatible with CrewAI?

Yes. You simply list the server URL in your mcps configuration. The framework treats it like any other external tool source, making deployment straightforward.

Q: If I need to audit video content using Videco and CrewAI, what should I do?

I'd assign a 'Reviewer' agent that calls listvideos for inventory. Then, another agent could call getvideoanalytics on key assets to ensure the entire operation is covered.

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Connect Videco MCP to CrewAI

Create your Vinkius account to connect Videco 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 Videco with CrewAI

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Autonomous Video Generation Agents

Assign a specialized agent the role of 'Video Creator'. This agent uses `create_video` to generate content based on research. The core crew structure handles the execution and reports status via `get_video`. The autonomous process ensures that video creation is part of a larger, multi-agent operation without needing manual triggering.

Collaborative Lead Capture & Analysis

Design a 'Lead Capture' agent that uses `list_leads` to gather contacts. A second 'Analysis' agent then takes the output and runs it through `get_video_analytics`. The crew shares memory, passing data between roles. This specialization allows for deep analysis of gathered lead information.

Full Campaign Lifecycle Monitoring

Set up a 'Monitor' agent that checks the system health using `check_videco_status`. Another agent can list all campaigns with `list_campaigns` and then escalate any issues found to an action agent. The hierarchical execution models ensure that one specialized role watches over the entire operation.

Setup guide

Set up Videco 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 Videco tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent Videco 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 Videco. 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 Videco MCP in CrewAI

You pass the MCP Server URL directly to your agents. This allows the crew—for example, a 'Campaign Manager' agent—to use tools like `create_campaign` as part of its assigned operational goal.

For full automation, you should expose both `list_leads` and `get_video_analytics`. This lets one agent research the leads while another analyzes their performance metrics automatically.

The server manages `Lead details`. When setting up your crew, you can assign agents to specifically handle and process this lead information autonomously. It's crucial specialized knowledge for the team.

Yes. You simply list the server URL in your `mcps` configuration. The framework treats it like any other external tool source, making deployment straightforward.

I'd assign a 'Reviewer' agent that calls `list_videos` for inventory. Then, another agent could call `get_video_analytics` on key assets to ensure the entire operation is covered.

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