How to Use the Deterministic Fair-Share Tip Splitter MCP in OpenAI Agents SDK

Q: How do I connect the Deterministic Fair-Share Tip Splitter to the OpenAI Agents SDK?

Install the openai-agents package via pip. Create an MCPServerStreamableHttp instance with your endpoint URL. Pass it to your agent's constructor using the mcpservers argument.

Give your OpenAI Agents SDK the math chops to split complex dinner checks down to the last penny.

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Connect Deterministic Fair-Share Tip Splitter MCP to OpenAI Agents SDK

Create your Vinkius account to connect Deterministic Fair-Share Tip Splitter 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 Deterministic Fair-Share Tip Splitter with OpenAI Agents SDK

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Claude

Perplexity

Precision billing with this MCP Server

The `split_bill` tool hands your agent the exact logic needed to distribute taxes and tips proportionally across a group. You pass a stringified JSON array of items, the tax amount, and the tip percentage. The server handles the heavy lifting to ensure nobody pays a massive tip on someone else's expensive steak. OpenAI Agents SDK makes this safe for production deployment. Built-in guardrails validate the inputs before the agent even attempts to run the math. You get full tracing in the OpenAI dashboard. When a user questions why they owe exactly $42.17, you can see the exact execution path.

Handling the fractional penny problem

The `split_bill` tool attacks the floating-point nightmare of splitting a ten-dollar tax across three people. It calculates individual shares and deterministically assigns the remaining fractional pennies to the highest totals. The math always adds up to the exact receipt total. Handoffs between specialized agents work beautifully here. You might have one agent parse a photo of the receipt using a vision model. That agent then passes the structured JSON off to a billing agent equipped with this tool. The transition is smooth, and the math is flawless.

Zero-config auto-discovery

Setting up the `split_bill` tool via MCP takes almost no effort. You just pass the endpoint to the agent constructor. The SDK auto-discovers the tool and makes it available for immediate use. Setting `cacheToolsList=True` keeps your agent fast. It skips the discovery phase on subsequent runs. This matters when you build real-time financial bots that need to respond before the dinner party breaks up.

Setup guide

Set up Deterministic Fair-Share Tip Splitter 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 Deterministic Fair-Share Tip Splitter tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Deterministic Fair-Share Tip Splitter tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Deterministic Fair-Share Tip Splitter 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="Deterministic Fair-Share Tip Splitter Agent",
            instructions="You have access to Deterministic Fair-Share Tip Splitter 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 Deterministic Fair-Share Tip Splitter MCP in OpenAI Agents SDK

Install the `openai-agents` package via pip. Create an `MCPServerStreamableHttp` instance with your endpoint URL. Pass it to your agent's constructor using the `mcp_servers` argument.

Yes, the SDK natively respects the tool schema. It validates the stringified JSON array and numeric inputs before executing the math, preventing bad data from crashing your bot.

You can easily restrict this tool to a specific finance agent. When a general concierge agent detects a receipt, it routes the task to the specialized agent to run the exact calculations.

Language models hallucinate floating-point arithmetic. This MCP server forces the agent to use deterministic code for the actual calculation, guaranteeing the final numbers sum perfectly.

We never store the itemized arrays, tax amounts, or tip percentages you send. The server processes the math in an ephemeral V8 isolate and immediately drops the memory. Your users' dining habits remain completely private.

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