How to Use the Whop MCP in Pydantic AI

Q: How do I retrieve Whop payment data with Pydantic AI?

You use listpayments. The agent validates the response, ensuring that every transaction record includes expected fields like amount and date. If anything is missing, it throws a clear validation error.

Q: What Whop details can I check with Pydantic AI?

You can retrieve user account info using getme. This tool's output is guaranteed to match your Python model. You won't waste time parsing unexpected data structures.

Q: Is cancelling a Whop membership safe with Pydantic AI?

Yes, calling cancelmembership triggers the action, but the resulting success or failure payload is validated against your schema. You get confirmation that the state change was recorded correctly.

Q: Can I list Whop reviews using Pydantic AI?

You call listreviews. The tool output is strictly typed, meaning you get predictable data structures for review text and star ratings. It keeps your code clean and reliable.

Ensure perfect Whop data integrity with Pydantic AI.

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Connect Whop MCP to Pydantic AI

Create your Vinkius account to connect Whop to Pydantic AI 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 Pydantic AI

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Type-Safe Subscription Status Checks

If you need to validate a membership, call `get_membership`. Because of the Pydantic validation layer, if the API returns fields that don't match your expected model (e.g., missing expiration date), the agent fails loudly. You never get silent corruption.

Handling Product and Plan Listings with Pydantic AI

To list all available products or plans, use `list_products` or `list_plans`. The response is immediately checked against your schema. This means you know precisely what fields to expect—no unexpected keys from the MCP Server.

Managing Discount Codes via Pydantic AI

When creating a promotional code, use `create_promo_code`. The Pydantic framework ensures that the resulting object (like the code ID or creation timestamp) conforms to your defined types. This is critical for reliable production pipelines.

Setup guide

Set up Whop MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "whop-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Whop tools.",
)

result = await agent.run("List recent Whop transactions")
print(result.output)

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Common questions about Whop MCP in Pydantic AI

You use `list_payments`. The agent validates the response, ensuring that every transaction record includes expected fields like amount and date. If anything is missing, it throws a clear validation error.

You can retrieve user account info using `get_me`. This tool's output is guaranteed to match your Python model. You won't waste time parsing unexpected data structures.

Yes, calling `cancel_membership` triggers the action, but the resulting success or failure payload is validated against your schema. You get confirmation that the state change was recorded correctly.

It touches payment transactions and membership details. Because of Pydantic validation, you're always working with structured types for `list_payments` and `get_membership`, eliminating ambiguity.

You call `list_reviews`. The tool output is strictly typed, meaning you get predictable data structures for review text and star ratings. It keeps your code clean and reliable.

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