How to Use the Gumlet MCP in AutoGen

Q: How do I register the Gumlet MCP Server with AutoGen?

Install autogen-extmcp and use the mcpservertools helper to fetch the tool definitions. Pass this list directly to your AssistantAgent constructor to give them access.

Q: Can AutoGen agents collaborate on Gumlet video uploads?

Yes, one agent can call createvideoupload to get the upload URL, while another agent monitors the upload status using getvideodetails.

Q: How do AutoGen agents handle Gumlet webhooks?

An agent can run listwebhooks to verify that active event handlers are correctly configured, discussing any missing endpoints with a security agent.

Deploy multi-agent AutoGen teams to automate Gumlet media processing, CDN analytics validation, and asset organization.

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Coordinate video pipelines with AutoGen agents

The `create_video_upload` tool initiates the video ingestion pipeline during multi-agent discussions. In an AutoGen team, one agent starts the upload while a separate quality-assurance agent calls `get_video_details` to verify the processing status. This collaborative execution ensures that files process fully before any user-facing steps occur. The agents debate progress and only proceed to thumbnail generation when the video is ready.

Validate CDN analytics through agent consensus

The `get_video_analytics` tool delivers real-time viewer engagement data to your conversational agents. A performance agent analyzes these stats while a billing agent cross-references them with organizational limits. The agents negotiate whether to flag high bandwidth usage or optimize existing assets. By discussing the raw data, they arrive at a consensus on whether to trigger alerts or adjust configurations.

Organize media libraries using an MCP Server

The `create_collection` tool allows your agent team to dynamically structure your media assets with this MCP Server. When a new project starts, the coordination agent creates a folder and directs other agents to upload files there. The team maintains order by running `list_video_collections` to check for existing folders before creating duplicates. This prevents messy, unorganized asset storage in your CDN account.

Setup guide

Set up Gumlet MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Gumlet tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Gumlet_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Gumlet data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Gumlet_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Gumlet data")
    print(result.messages[-1].content)

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 AutoGen

Install `autogen-ext[mcp]` and use the `mcp_server_tools` helper to fetch the tool definitions. Pass this list directly to your `AssistantAgent` constructor to give them access.

Yes, one agent can call `create_video_upload` to get the upload URL, while another agent monitors the upload status using `get_video_details`.

An agent can run `list_webhooks` to verify that active event handlers are correctly configured, discussing any missing endpoints with a security agent.

Yes, you can filter the tool list before passing it to the agent, removing tools like `delete_video` to ensure your media library remains safe.

All folder names and collection metadata are processed locally within ephemeral sandboxed environments. The server uses point-to-point encryption for all API traffic, preventing unauthorized access to your media hierarchy.

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