How to Use the Mollie MCP in OpenAI Agents SDK

Q: How do I connect Mollie to my OpenAI Agents SDK project?

You instantiate an MCPServerStreamableHttp object with your endpoint and add it to your agent constructor. The SDK automatically maps the Mollie tools to your agent's function calls.

Q: Can the OpenAI Agents SDK handle Mollie refund data?

Yes. Your agent uses listrefunds to pull transaction history. You can then write logic to process these refund objects according to your internal business rules.

Q: How does the agent handle payment methods with Mollie?

The agent calls listpaymentmethods to see what local options are available. It then presents these choices to your end user based on the real-time data returned.

Handle European payments directly in your Python agent using the Mollie MCP Server and the OpenAI Agents SDK.

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Connect Mollie MCP to OpenAI Agents SDK

Create your Vinkius account to connect Mollie to OpenAI Agents SDK 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 Mollie with OpenAI Agents SDK

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Automate payment creation with OpenAI Agents SDK

Your agent triggers `create_payment` to handle transaction requests without leaving your Python environment. It manages the full lifecycle of a payment from initiation to redirect. By using the agent's native tool discovery, you skip manual boilerplate code. Your agent receives the payment URL directly from the Mollie response.

Verify transaction states in your agent flow

Use `get_payment_details` to check the current status of any transaction within your production agent system. It provides the exact state required for your guardrail logic. You avoid manual polling by letting your agent query the status only when needed. The tracing features in your SDK record these calls for debugging.

Manage recurring billing via MCP Server

The agent retrieves active subscriptions by calling `list_customer_subscriptions` to reconcile customer data. It ensures your billing records stay in sync with the Mollie platform. This keeps your agent loop aware of ongoing customer agreements. You can then trigger follow-up actions based on the subscription data returned.

Setup guide

Set up Mollie MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Mollie tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Mollie tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Mollie tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Mollie Agent",
            instructions="You have access to Mollie tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Mollie MCP in OpenAI Agents SDK

You instantiate an `MCPServerStreamableHttp` object with your endpoint and add it to your agent constructor. The SDK automatically maps the Mollie tools to your agent's function calls.

Yes. Your agent uses `list_refunds` to pull transaction history. You can then write logic to process these refund objects according to your internal business rules.

Absolutely. You point your agent to a local instance of the server during development. This allows you to verify tool execution in the OpenAI Agents SDK before going to production.

The agent calls `list_payment_methods` to see what local options are available. It then presents these choices to your end user based on the real-time data returned.

The MCP Server returns a standard error code that your agent catches. You should implement basic retry logic in your Python code to handle transient network issues.

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