How to Use the Brankas MCP in OpenAI Agents SDK

Q: How do I configure Brankas in my OpenAI Agents SDK project?

Install the package using pip install openai-agents and initialize MCPServerStreamableHttp with your Vinkius endpoint. Pass the server instance into the mcpservers list of your Agent constructor and set cacheToolsList=True to speed up tool discovery.

Trigger Southeast Asian payouts and check balances directly from your OpenAI Agents SDK production workflows.

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Connect Brankas MCP to OpenAI Agents SDK

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

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Automate Brankas transfers via OpenAI Agents SDK

This setup uses the `inter_bank_transfer` and `intra_bank_transfer` tools to execute payouts directly from your script. Your python agent handles the routing logic, choosing the right transfer path based on the recipient's bank routing data. Because OpenAI Agents SDK runs with strict guardrails, you can set hard limits on transfer amounts before the agent triggers the transaction. The agent verifies the destination, checks the balance, and pushes the payout through without manual intervention.

Verify identity and balances in real-time

This system uses `get_balance` and `get_identities` to pull real-time financial data before initiating any checkout flows. Your agent queries the customer's linked account to confirm they have sufficient funds and that the name matches their profile. The OpenAI Agents SDK maps these tool outputs to its tracing dashboard, letting you audit exactly when and why an agent checked a balance. You get a clear, step-by-step log of the identity verification process for every transaction.

Build self-monitoring payment flows with this MCP Server

This feature uses `create_checkout` and `get_transaction` to spin up payment links and monitor their settlement status. Your agent creates the checkout session, hands off the URL to the user, and polls the payment status in the background. The SDK handles these long-running polling loops by passing the state between specialized monitoring agents. If a transaction fails, the agent immediately flags the specific checkout ID in your OpenAI dashboard for quick debugging.

Setup guide

Set up Brankas 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 Brankas tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Brankas 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="Brankas Agent",
            instructions="You have access to Brankas tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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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 Brankas. 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 Brankas MCP in OpenAI Agents SDK

Install the package using `pip install openai-agents` and initialize `MCPServerStreamableHttp` with your Vinkius endpoint. Pass the server instance into the `mcp_servers` list of your Agent constructor and set `cacheToolsList=True` to speed up tool discovery.

Yes, but you should use the SDK's built-in guardrails to validate the transfer arguments before execution. You can write a validation hook that checks the transfer amount against a hardcoded limit in your Python code.

The SDK relies on your agent's retry logic and the underlying MCP client to handle rate limits. When a statement call returns a rate-limiting status, the agent pauses and retries the request based on your backoff configuration.

You use the checkout creation tool to generate the session and save the ID. Then, have a specialized polling agent run status checks periodically, utilizing the SDK's agent-to-agent handoff to keep your main conversational agent free.

Your bank statements and balance data never pass through third-party logging servers. The Vinkius MCP sandbox isolates the execution, and the SDK transmits the data directly to your local runtime over secure, encrypted transport.

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