How to Use the Kelley Blue Book Valuation MCP in AutoGen

Q: What's an example of a multi-agent workflow with Kelley Blue Book Valuation and AutoGen?

You could have a 'Scout' agent use searchvehicles to find cars meeting certain criteria. It then passes the list to a 'Valuation' agent, which calls getvehiclevaluation on each one. A final 'Manager' agent reviews the conversation and approves the best deal.

Set up teams of AutoGen agents to debate, analyze, and agree on vehicle valuations using live KBB data.

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Connect Kelley Blue Book Valuation MCP to AutoGen

Create your Vinkius account to connect Kelley Blue Book Valuation to AutoGen 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 AutoGen

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Enable Agent Teams to Debate Valuations

This is more than just tool use; it's collaborative problem-solving. You can create a 'Buyer' agent that uses `get_vehicle_valuation` to argue for a low trade-in price, and a 'Seller' agent that uses `get_market_trends` to push for a higher price. They'll converse, challenge each other's data, and work towards a consensus. AutoGen lets you model a real negotiation. The agents can go back and forth, using tools like `get_vehicle_details` to pull more data to support their arguments. The final decision is a result of that structured debate, not a single API call.

Assign KBB Tools to Specialist Agents

Build a multi-agent workflow for vehicle analysis. An 'Analyst' agent can be given access to `get_market_trends` to provide high-level context. A 'Mechanic' agent could be responsible for using `get_vehicle_by_vin` to check for factory specs. A 'Finance' agent gets the final say with `get_vehicle_valuation`. With this MCP Server, you can restrict which agent can use which tool. This creates a separation of duties where agents with different roles collaborate to assess a vehicle. The conversation history shows exactly how they reached their conclusion.

Automate Complex Due Diligence

Give your agent team a task like, "Find the best-value 2022 SUV under $30,000." One agent can use `search_vehicles` to generate a list of candidates. Another agent can then loop through that list, calling `get_vehicle_valuation` on each one. A third agent can summarize the findings. This isn't a simple script; it's a dynamic conversation. If a valuation comes back unexpectedly high, the agents can discuss why, perhaps calling `get_market_trends` to see if the segment is currently overpriced. It's an automated research team.

Setup guide

Set up Kelley Blue Book Valuation MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Kelley Blue Book Valuation tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Kelley Blue Book Valuation_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Kelley Blue Book Valuation data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Kelley Blue Book Valuation_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Kelley Blue Book Valuation data")
    print(result.messages[-1].content)

Get your connection token →

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 AutoGen

You instantiate the tools from the MCP server and pass them to your AssistantAgent. The agents can then invoke tools like `get_vehicle_valuation` as functions within their conversation, using the results to inform their replies and decisions.

Yes, that's a primary use case. You can have one agent pull the trade-in value with `get_vehicle_valuation` and another pull fair market retail, then have them debate the correct price for a specific transaction.

You could have a 'Scout' agent use `search_vehicles` to find cars meeting certain criteria. It then passes the list to a 'Valuation' agent, which calls `get_vehicle_valuation` on each one. A final 'Manager' agent reviews the conversation and approves the best deal.

The agent conversation, including any data fetched from this MCP server, happens within your AutoGen environment. The Vinkius-managed server only sees individual, stateless tool requests and has no access to the context of your agent chats.

The MCP server only processes the immediate request, like a lookup for a specific VIN. It retains no memory of the call. The data itself—the VIN, make, model, and valuation—exists within your agents' conversation history, which you control completely.

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