How to Use the Whop MCP in LlamaIndex

Q: How do I find details on canceled memberships using Whop and LlamaIndex?

You first execute cancelmembership via the MCP Server. Then, you index the resulting data alongside listmemberships. Your agent can then answer follow-up questions about that specific cancellation event.

Q: Can LlamaIndex track financial changes with Whop?

Absolutely. You query the index using results from listpayments and listinvoices. This allows you to build an answer grounded in exact transaction amounts and dates, making it perfect for finance reports.

Q: What if I need to check promo codes with Whop and LlamaIndex?

You run listpromocodes first. Indexing this list allows your agent to answer questions like, 'Which old promo code was used for the Acme Corp account?' The index keeps a searchable history.

Q: Do I have to manually combine product data with Whop and LlamaIndex?

No. By indexing output from listproducts and listplans, your agent automatically combines this information. You ask a question, and it pulls context from both sets of rules.

Q: What data types are available for advanced searching with Whop and LlamaIndex?

You can build search indexes over membership details (getmembership), payment transactions (listpayments), and all customer invoices (listinvoices). This gives you a robust, queryable history.

Ground your AI responses in factual data from Whop using LlamaIndex and the MCP Server.

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Connect Whop MCP to LlamaIndex

Create your Vinkius account to connect Whop to LlamaIndex 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 LlamaIndex

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Build a searchable membership knowledge base

LlamaIndex indexes all available member records for deep searching. You can run `list_memberships` and feed those results into your vector store. Later, you query the index to ask questions like, 'What services did John Doe cancel?' The answer is grounded in the data from `list_memberships`. This capability means your agent doesn't guess; it retrieves factual answers directly from past Whop MCP Server calls. You combine this structured API data with internal documents for maximum context.

Analyze payment history and billing issues

Want to answer complex questions about customer spending? Index the results of `list_payments` and `list_invoices`. Instead of just getting a list, your agent can semantically search this data. You ask, 'Why was there a charge last month?' and it pulls context from both payment records and invoices. This indexing approach lets you combine billing history with product details (`list_products`) to pinpoint the exact source of confusion for support staff.

Manage and review product information

Use LlamaIndex to create a knowledge base around your entire catalog. Indexing results from `list_plans` gives you an answer like, 'What is the difference between Basic and Pro?' You can also index all reviews using `list_reviews`. This allows for sophisticated comparison queries. When someone asks about a specific product, your agent first checks `list_products`, then uses that data to provide comprehensive answers grounded in both plans and user feedback.

Setup guide

Set up Whop MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Whop MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Whop tools.",
)
response = await agent.run("List recent Whop data")

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Common questions about Whop MCP in LlamaIndex

You first execute `cancel_membership` via the MCP Server. Then, you index the resulting data alongside `list_memberships`. Your agent can then answer follow-up questions about that specific cancellation event.

Absolutely. You query the index using results from `list_payments` and `list_invoices`. This allows you to build an answer grounded in exact transaction amounts and dates, making it perfect for finance reports.

You run `list_promo_codes` first. Indexing this list allows your agent to answer questions like, 'Which old promo code was used for the Acme Corp account?' The index keeps a searchable history.

No. By indexing output from `list_products` and `list_plans`, your agent automatically combines this information. You ask a question, and it pulls context from both sets of rules.

You can build search indexes over membership details (`get_membership`), payment transactions (`list_payments`), and all customer invoices (`list_invoices`). This gives you a robust, queryable history.

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