How to Use the Fuel Cost Calculator MCP in OpenAI Agents SDK

Build production agents with Fuel Cost Calculator using OpenAI Agents SDK. Drive smarter fuel decisions with built-in validation.

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Connect Fuel Cost Calculator MCP to OpenAI Agents SDK

Create your Vinkius account to connect Fuel Cost Calculator to OpenAI Agents SDK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Fuel Cost Calculator with OpenAI Agents SDK

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Calculate precise trip expenses

Pass your trip variables into `calculate_trip_cost` to get exact figures for your agents. This tool provides the raw data needed for your agent's decision-making logic. It fits right into your existing Python agent flow. You get reliable, repeatable cost analysis without extra overhead.

Optimize fuel choice automatically

Your agent can trigger `compare_fuel_efficiency` to weigh fuel types against current market prices. This ensures your agent selects the most cost-effective option for the user. It reduces the complexity of manual price checks. Your code stays clean while the agent handles the comparison logic.

Summarize trip logistics via agent

Call `get_trip_summary` to provide the user with a final, readable report on their travel costs. It consolidates all the math into a single, actionable response. This is ideal for agents that manage travel planning or budget tracking. It delivers a concise summary that the user can trust.

Setup guide

Set up Fuel Cost Calculator 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 Fuel Cost Calculator tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Fuel Cost Calculator 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="Fuel Cost Calculator Agent",
            instructions="You have access to Fuel Cost Calculator 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 Fuel Cost Calculator. 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 Fuel Cost Calculator MCP in OpenAI Agents SDK

Initialize the MCPServerStreamableHttp object and pass it to your Agent constructor. The SDK discovers the tools automatically.

Yes, you can set cacheToolsList to True when configuring your agent. This improves performance by storing tool definitions locally.

Agents can hand off the trip cost results to other specialized agents. This is standard behavior for your production agent system.

The MCP does not persist your route history. Data is handled within the memory of your agent session and cleared after the request.

The tools process fuel efficiency and price per gallon. No private user telemetry is accessed, keeping your agent's input strictly to the financial metrics provided.

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