How to Use the Afterpay MCP in LlamaIndex

Q: Can I index Afterpay transaction histories with LlamaIndex?

Yes, you use listpayments to pull historical BNPL logs and feed them into your document index. This lets you run semantic search queries over your payment history.

Q: How does LlamaIndex verify Afterpay transaction limits?

The agent calls getafterpayconfig to fetch active merchant limits. It indexes these constraints so it can instantly flag carts that fall outside the allowed payment range.

Q: Can this Afterpay MCP Server help resolve refund disputes in LlamaIndex?

Yes, the agent retrieves transaction logs using getpaymentdetails to verify the refund balance. It uses this live context to answer customer questions accurately without hallucinating.

Q: How do I register these payment tools in my LlamaIndex pipeline?

You load the server using McpToolSpec and convert the endpoints into a list of tools. Your agent then calls tools like createcheckout as needed during a query loop.

Index live Afterpay transaction logs and checkout configurations directly into your LlamaIndex vector store.

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Connect Afterpay MCP to LlamaIndex

Create your Vinkius account to connect Afterpay 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 Afterpay with LlamaIndex

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Index Afterpay payment logs into LlamaIndex RAG

The `list_payments` tool extracts your transaction history so LlamaIndex can build a queryable vector store from your payment records. This tool feeds transaction details into your data index, letting your agent answer questions about past orders using real-time API data. You query your financial index to find specific settlements without writing SQL. The agent searches the indexed tool outputs to locate historical records instantly.

Ground checkout decisions using live configuration data

The `get_afterpay_config` tool pulls your active merchant transaction limits directly into your LlamaIndex knowledge base using this MCP connection. This tool ensures your agent knows the exact minimum and maximum order limits before it attempts to initiate a checkout. By indexing this configuration, the agent avoids hallucinating payment terms. It matches user cart values against the retrieved limits to decide if a checkout is valid.

Audit refunds through this MCP Server

The `get_payment_details` tool retrieves the financial status and settlement logs of any specific order ID for your index. This tool feeds the precise refund balance and transaction state into your query pipeline, ensuring your support agent has current facts. If a customer asks about a refund, the agent uses `refund_payment` to issue the credit, then indexes the new receipt. This keeps your search index in sync with actual bank movements.

Setup guide

Set up Afterpay 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 Afterpay 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 Afterpay tools.",
)
response = await agent.run("List recent Afterpay data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Afterpay. 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 Afterpay MCP in LlamaIndex

Yes, you use `list_payments` to pull historical BNPL logs and feed them into your document index. This lets you run semantic search queries over your payment history.

The agent calls `get_afterpay_config` to fetch active merchant limits. It indexes these constraints so it can instantly flag carts that fall outside the allowed payment range.

Yes, the agent retrieves transaction logs using `get_payment_details` to verify the refund balance. It uses this live context to answer customer questions accurately without hallucinating.

You load the server using `McpToolSpec` and convert the endpoints into a list of tools. Your agent then calls tools like `create_checkout` as needed during a query loop.

Your merchant account keys and payment token strings stay inside the secure Vinkius runtime. The server only passes the necessary order IDs and transaction statuses to your index, keeping sensitive financial details isolated from external storage.

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