How to Use the Afterpay MCP in LangChain

Q: How do I pass Afterpay limits into a LangChain chain?

You call getafterpayconfig inside your chain setup to fetch active account boundaries. The agent reads these numbers and decides whether to trigger a checkout or flag the order.

Q: Can my LangChain agent automatically refund an Afterpay order?

Yes, your agent uses refundpayment to credit the buyer's account. It first checks getpaymentdetails to verify the remaining balance before executing the refund.

Q: How does this Afterpay MCP Server handle LangSmith tracing?

Every call to tools like listpayments gets logged in LangSmith with precise latency and input payloads. You see exactly what transaction data your agent requested and how the API responded.

Q: Do I need custom API wrappers to run Afterpay checkouts in LangChain?

No, you use the standard MultiServerMCPClient to connect directly to this server. It registers all payment tools as native LangChain tools in one line of code.

Run Afterpay transactions directly within your LangChain chains to automate checkouts and track payment statuses.

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Connect Afterpay MCP to LangChain

Create your Vinkius account to connect Afterpay to LangChain 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 LangChain

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Build multi-step Afterpay checkout chains in LangChain

The `create_checkout` tool initiates a secure Afterpay BNPL payment session token directly within your active LangChain execution graph. Your agent uses this tool to start a purchase, then passes the resulting token to the next step in your chain without manual data passing. By monitoring this chain in LangSmith, you watch the agent fetch transaction limits via `get_afterpay_config` to verify the order fits the account boundaries before it ever hits the checkout endpoint. This stops failed payment runs before they consume API calls.

Trace Afterpay refund pipelines using LangChain

The `refund_payment` tool writes immediate credits back to a customer's card after verifying their remaining balance. This tool works within a LangGraph agent that checks the purchase history first to make sure you do not issue duplicate refunds. You get full observability on every refund step, seeing the exact inputs and outputs of `get_payment_details` recorded in your LangSmith dashboard. It removes the guesswork from complex payment reversals by logging the raw API responses.

Track payment logs through this MCP Server

The `list_payments` tool pulls your historical transaction logs so your LangChain agent can audit past settlements. This tool feeds raw transaction histories directly into your agent's context, letting it analyze payment patterns or flag anomalies. Connecting this MCP Server to your multi-agent setup lets you combine payment logs with your internal support databases. Your agent checks the status of any transaction instantly without you writing custom API glue code.

Setup guide

Set up Afterpay MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Afterpay tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "afterpay-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Afterpay transactions"
    })
    print(result["messages"][-1].content)

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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 LangChain

You call `get_afterpay_config` inside your chain setup to fetch active account boundaries. The agent reads these numbers and decides whether to trigger a checkout or flag the order.

Yes, your agent uses `refund_payment` to credit the buyer's account. It first checks `get_payment_details` to verify the remaining balance before executing the refund.

Every call to tools like `list_payments` gets logged in LangSmith with precise latency and input payloads. You see exactly what transaction data your agent requested and how the API responded.

No, you use the standard `MultiServerMCPClient` to connect directly to this server. It registers all payment tools as native LangChain tools in one line of code.

This MCP Server runs in an isolated V8 sandbox and never stores your merchant API keys or customer payment tokens. It handles transaction data ephemerally, passing order IDs and refund amounts directly to Afterpay without caching them on disk.

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