How to Use the zipperHQ MCP in AutoGen

Q: Do I need AutoGen for zipperHQ analytics?

No. You can use single calls to getvideoanalytics directly. However, using AutoGen lets you build systems where multiple agents debate how the analytics should be interpreted or acted upon. It's about consensus-driven action.

Q: Is it possible to check zipperHQ status with AutoGen?

Yes, an agent can be assigned the task of running checkzipperstatus. The result is then passed to another agent for interpretation—'Should we proceed given this status?' This adds a layer of reasoning above simple execution.

Facilitate consensus on communication strategy using AutoGen with zipperHQ.

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Connect zipperHQ MCP to AutoGen

Create your Vinkius account to connect zipperHQ to AutoGen 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 zipperHQ with AutoGen

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Perplexity

Debate Video Content Strategy

Set up multiple agents to discuss video content. One agent can call `list_contacts` for a list of recipients, and another can call `get_video_analytics` on the top performers. The agents then debate: 'Should we focus more on recordings or polished videos?'—using real metrics as evidence.

Verify Messaging System Health

A 'Security Agent' can run `check_zipper_status` to confirm the platform is stable. A 'Reporting Agent' then runs `get_account` to pull baseline data. The debate converges on a recommendation: proceed or pause, based on system integrity and current account status.

Manage Content Lifecycle

One agent can use `list_videos` to get the full roster. A second agent uses `get_recent_videos` to find the latest stuff. They negotiate which videos need review, leading to a prioritized list of content for human action.

Setup guide

Set up zipperHQ 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 zipperHQ 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="zipperHQ_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent zipperHQ 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="zipperHQ_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent zipperHQ 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 zipperHQ. 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 zipperHQ MCP in AutoGen

The multi-agent system lets you assign roles. One agent uses `get_video` to fetch specific details, and another agent challenges those details against the contact list. The outcome is a verified understanding of the asset's metadata.

No. You can use single calls to `get_video_analytics` directly. However, using AutoGen lets you build systems where multiple agents debate *how* the analytics should be interpreted or acted upon. It's about consensus-driven action.

The MCP Server exposes contact lists, video records, and usage metrics. The agents use these distinct streams of communication data to build a complete picture. This gives the debate realistic boundaries.

Yes, an agent can be assigned the task of running `check_zipper_status`. The result is then passed to another agent for interpretation—'Should we proceed given this status?' This adds a layer of reasoning above simple execution.

The server touches contact details, video viewing metrics, and general account information. The agents work together to process these different communication data streams simultaneously. This ensures all aspects of the relationship are considered.

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