How to Use the Whop MCP in AutoGen

Q: How do I check membership status with Whop and AutoGen?

You initiate the process by having one agent call getmembership. A second agent then challenges this result, forcing a confirmation against the full list using listmemberships. This consensus ensures accuracy.

Q: Can I use AutoGen for payment reconciliation with Whop?

Yes. You set up agents to compare data from listpayments and listinvoices. They debate any differences in transaction amounts or dates until a single, verified conclusion is reached.

Q: What if I need help with product listings using Whop and AutoGen?

You can deploy agents to review the results of listproducts and listplans. One agent checks for outdated items, while another verifies pricing consistency. The negotiation finds the optimal state.

Q: Which tools are needed to manage user accounts with Whop and AutoGen?

You'll use a combination of getmembership, listmemberships, and potentially cancelmembership. The power comes from the agents debating which tool call is safest or most appropriate.

Q: What data types are touched when running an audit with Whop and AutoGen?

The core financial datasets include payment transactions (listpayments) and customer invoices (listinvoices). The agents debate these structured records to ensure the final report is completely accurate.

Build consensus-driven decision systems for Whop using AutoGen and the MCP Server.

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Create your Vinkius account to connect Whop 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 Whop with AutoGen

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Validate membership status through debate

You can set up two agents: a 'Status Checker' agent and an 'Action Agent.' The first calls `get_membership` to fetch current data. The second challenges that data, asking if the user should instead be reviewed via `list_memberships`. The final decision is a consensus on the necessary action. This debate framework ensures you don't just execute; multiple perspectives check for edge cases before calling tools like `cancel_membership`.

Process complex billing disputes

Use AutoGen to simulate a financial audit. One agent lists invoices using `list_invoices`, while another lists payments via `list_payments`. They debate any discrepancies they find in the dates or amounts. This negotiation process resolves ambiguities before you run a final report. It's not just listing; it’s making multiple agents argue over whether the data from `list_plans` matches the actual charges.

Develop multi-stage promo code deployment

Run three agents: 'Marketing,' 'Technical,' and 'Compliance.' The Marketing agent proposes a discount using `create_promo_code`. The Technical agent checks that it works with existing plans by calling `list_plans`. Finally, the Compliance agent verifies the output against all current codes using `list_promo_codes`. This system forces deliberation. You don't just create a code; you get consensus on its viability across multiple organizational viewpoints.

Setup guide

Set up Whop 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 Whop 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="Whop_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

You initiate the process by having one agent call `get_membership`. A second agent then challenges this result, forcing a confirmation against the full list using `list_memberships`. This consensus ensures accuracy.

Yes. You set up agents to compare data from `list_payments` and `list_invoices`. They debate any differences in transaction amounts or dates until a single, verified conclusion is reached.

You can deploy agents to review the results of `list_products` and `list_plans`. One agent checks for outdated items, while another verifies pricing consistency. The negotiation finds the optimal state.

You'll use a combination of `get_membership`, `list_memberships`, and potentially `cancel_membership`. The power comes from the agents debating which tool call is safest or most appropriate.

The core financial datasets include payment transactions (`list_payments`) and customer invoices (`list_invoices`). The agents debate these structured records to ensure the final report is completely accurate.

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