How to Use the Gumlet MCP in CrewAI

Q: How do I check my Gumlet usage in CrewAI?

Your agents can query getaccountinfo as part of their routine. They can then take action if your storage or bandwidth limits are nearing the threshold.

Deploy autonomous agent crews for Gumlet management. Let your CrewAI team handle video uploads, analytics, and library maintenance.

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Connect Gumlet MCP to CrewAI

Create your Vinkius account to connect Gumlet 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 Gumlet with CrewAI

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Collaborative media asset operations

Assign research agents to use `list_videos` and `get_video_analytics` to find content trends. They pass this data to action agents that use `update_video_thumbnail` to optimize engagement. Your crew works together to maintain your media presence. Each agent knows exactly which tool to call to get the job done.

Automated video delivery monitoring

Have a monitor agent check `list_webhooks` and `get_video_details` continuously. If it detects a processing delay, it triggers a correction or alerts your human moderator. This creates a closed-loop system for your CDN pipeline. You don't have to watch the dashboard yourself.

Autonomous library maintenance

Configure your crew to prune old content using `delete_video` based on insights from `get_video_analytics`. The agents ensure your library stays organized as new uploads arrive via `create_video_upload`. This keeps your media storage costs predictable. It runs in the background while your team focuses on creating content.

Setup guide

Set up Gumlet 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 Gumlet tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Pass the server endpoint directly into your agent's configuration. The crew will automatically discover and utilize the available Gumlet tools.

Yes, you can define autonomous workflows. Your crew performs the analysis and executes the necessary updates based on your instructions.

The server uses Vinkius-managed auth. Your agent only gets access to the tools you explicitly authorize through the endpoint token.

Your agents can query `get_account_info` as part of their routine. They can then take action if your storage or bandwidth limits are nearing the threshold.

It accesses your video metadata and account-level telemetry. Every request is isolated and audited to ensure your configuration remains untouched.

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