How to Use the GAN.ai MCP in CrewAI

Q: How do I filter GAN.ai tools for specific CrewAI agents?

Use the toolfilter parameter in your MCP configuration. This prevents agents from accessing tools they don't need, keeping the crew focused.

Coordinate specialized agent crews to research, generate, and monitor video campaigns using the GAN.ai MCP Server.

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Connect GAN.ai MCP to CrewAI

Create your Vinkius account to connect GAN.ai 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 GAN.ai with CrewAI

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Deploy autonomous crews with GAN.ai

Assign a researcher agent to fetch template schemas via `get_project_metadata` while a creator agent executes `generate_personalized_videos`. Your crew collaborates to produce content without manual intervention. Use `list_landing_templates` to ensure your agents are using the most current assets. This keeps your video production aligned with your active marketing campaigns.

Monitor and verify video generation

Task your monitor agent with checking `get_generation_status` to track progress across the entire crew. If a job stalls, the agent can escalate the issue or retry the call. Use `verify_api_connection` as an initial handshake for your agents. This ensures the crew is ready to work before they start consuming workspace resources.

Analyze performance across the crew

Aggregate engagement data with `get_video_stats` so your analyst agent can report on campaign success. This allows the crew to iterate on their strategy based on hard numbers. Review project history using `list_generated_videos` to maintain a log of all actions. Your agents can reference these logs to avoid repeating past mistakes in future sequences.

Setup guide

Set up GAN.ai 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 GAN.ai tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Yes, pass the server URL directly into your agent's MCP list. This allows the crew to invoke video generation tools as part of their assigned tasks.

Use the tool_filter parameter in your MCP configuration. This prevents agents from accessing tools they don't need, keeping the crew focused.

The server provides a shared interface. Any agent in the crew can query `get_workspace_info` to see the current state of your projects and templates.

Check the agent logs for tool output. You can also have a monitor agent run `verify_api_connection` to confirm the server is responsive.

Your video generation parameters and project data are restricted to your workspace. The MCP server treats each agent interaction as an authenticated request tied to your user token.

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