How to Use the Kelley Blue Book Valuation MCP in OpenAI Agents SDK

Q: How do I use the Kelley Blue Book Valuation tools with the OpenAI Agents SDK?

Just pass the Vinkius MCP endpoint to the MCPServerStreamableHttp constructor. Your agent auto-discovers all tools like getvehiclevaluation and knows how to call them. You don't need to define them yourself.

Q: Can my OpenAI agent get KBB trade-in prices for a list of VINs?

Yes. Your agent can loop through a list of VINs and call getvehiclebyvin followed by getvehiclevaluation for each one. The SDK's async support handles the concurrent requests efficiently.

Q: What's the best way to get a Kelley Blue Book Valuation for a car without a VIN using the OpenAI Agents SDK?

Your agent should use the searchvehicles tool with the year, make, and model. From there, it can use listvehicletrims and listtrimstyles to narrow down the exact configuration before calling getvehiclevaluation.

Get real-time KBB trade-in values inside your OpenAI Agents SDK, with built-in guardrails for every API call.

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Connect Kelley Blue Book Valuation MCP to OpenAI Agents SDK

Create your Vinkius account to connect Kelley Blue Book Valuation 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 Kelley Blue Book Valuation with OpenAI Agents SDK

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Get Exact Valuations, Not Guesses

This toolset connects your agent directly to Kelley Blue Book's valuation engine. Use `get_vehicle_by_vin` for a quick lookup or chain `search_vehicles` and `list_vehicle_trims` to pinpoint the exact car you need a price on. The agent finds the car, you get the numbers. Once you have the vehicle ID, `get_vehicle_valuation` pulls the trade-in and fair market range. Since the OpenAI Agents SDK handles tool discovery automatically, you just ask for the value. The agent figures out the rest.

Build Safe, Multi-Agent Workflows

The real power here is coordinating agents. One agent can specialize in discovery, using `list_makes_by_year` and `list_models_by_make` to explore inventory. It then hands off a list of vehicle IDs to a valuation agent. The SDK's guardrails mean you can approve the valuation step before it runs. You get full visibility in the OpenAI dashboard, seeing exactly which agent called `get_market_trends` and what data it got back. No surprises.

Turn Market Data into Actions

Go beyond single-car lookups. Your agent can use `get_market_trends` to check for pricing shifts on a specific model before deciding to run a batch valuation on your inventory. It's about making smarter calls, not just faster ones. This MCP Server exposes the tools, and your agent provides the logic. You can build a system that alerts you when trade-in values for SUVs in your region drop, all traced and managed through your agent framework.

Setup guide

Set up Kelley Blue Book Valuation 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 Kelley Blue Book Valuation tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Kelley Blue Book Valuation 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="Kelley Blue Book Valuation Agent",
            instructions="You have access to Kelley Blue Book Valuation 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 Kelley Blue Book. 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 Kelley Blue Book Valuation MCP in OpenAI Agents SDK

Just pass the Vinkius MCP endpoint to the `MCPServerStreamableHttp` constructor. Your agent auto-discovers all tools like `get_vehicle_valuation` and knows how to call them. You don't need to define them yourself.

Yes. Your agent can loop through a list of VINs and call `get_vehicle_by_vin` followed by `get_vehicle_valuation` for each one. The SDK's async support handles the concurrent requests efficiently.

Your agent should use the `search_vehicles` tool with the year, make, and model. From there, it can use `list_vehicle_trims` and `list_trim_styles` to narrow down the exact configuration before calling `get_vehicle_valuation`.

Absolutely. It's built for it. When your agent calls a tool like `get_market_trends`, you'll see the run steps and results stream back in real time, just like you'd expect.

Your request, including any VINs or vehicle details, is sent to an ephemeral Vinkius sandbox that runs the MCP server. The sandbox only exists for the transaction, Vinkius handles the secure call to KBB, and then the instance is destroyed. Your data isn't stored.

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