How to Use the Kelley Blue Book Valuation MCP in Google ADK

Q: How do I connect the Kelley Blue Book Valuation MCP Server to my Google ADK agent?

You'll import McpToolset and point it to your Vinkius MCP Server URL. Then you pass that toolset into your LlmAgent's tool list. The agent will then have all KBB tools ready to go.

Q: What's the process for getting a Kelley Blue Book Valuation with Google ADK if I only know the make and model?

Your agent will first call searchvehicles. It will then likely need to ask clarifying questions or use listvehicletrims to identify the exact vehicle before it can fetch an accurate price with getvehiclevaluation.

Q: Can I restrict which KBB tools my Google ADK agent has access to?

Yes. When you create the McpToolset, you can pass in an optional toolnames filter. This lets you expose only specific functions, like getvehiclevaluation, while hiding others.

Connect Google ADK to KBB data. Run large-scale vehicle valuations and analysis right inside your Google Cloud environment.

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Connect Kelley Blue Book Valuation MCP to Google ADK

Create your Vinkius account to connect Kelley Blue Book Valuation to Google ADK 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 Google ADK

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Query KBB Data Like a Database

This isn't just a simple lookup tool. With Google ADK, you can treat the Kelley Blue Book database as an extension of your own data warehouse. Your Gemini agent can pull a list of models from a BigQuery table and use `list_vehicle_trims` to find every available configuration. The long-context window means you can reason over thousands of results at once. Ask your agent to find the top 10 most valuable sedan trims from 2022 using `get_vehicle_valuation` across the board. It will hold the context and give you a straight answer.

Integrate Valuations into Vertex AI

Your agent, running on Google's infrastructure, can now access real-world financial data. Use the `get_market_trends` tool to feed current pricing shifts directly into your Vertex AI prediction models. You don't need to set up a separate data pipeline. Just instantiate the `McpToolset` with the Vinkius URL. Your LlmAgent now has access to tools like `get_vehicle_by_vin` and can enrich your datasets on the fly.

Act on Enterprise-Scale Vehicle Data

Stop dealing with stale CSV exports. Give your agent a task: 'Analyze the trade-in value of our current F-150 inventory against market trends.' The agent will use `search_vehicles` and `get_vehicle_valuation` for your trucks and `get_market_trends` for the bigger picture. Because this all runs within the Google ecosystem, it's built for scale. This MCP server gives your agent the specific functions it needs to operate on vehicle data, whether it's for one car or a million.

Setup guide

Set up Kelley Blue Book Valuation MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Kelley Blue Book Valuation tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Kelley Blue Book Valuation_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Kelley Blue Book Valuation tools via MCP.",
    tools=mcp_tools,
)

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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 Google ADK

You'll import `McpToolset` and point it to your Vinkius MCP Server URL. Then you pass that toolset into your `LlmAgent`'s tool list. The agent will then have all KBB tools ready to go.

Yes, that's a primary use case. Your agent can read VINs from BigQuery, call `get_vehicle_valuation` for each one via this MCP server, and even write the results back to a new table.

Your agent will first call `search_vehicles`. It will then likely need to ask clarifying questions or use `list_vehicle_trims` to identify the exact vehicle before it can fetch an accurate price with `get_vehicle_valuation`.

Yes. When you create the `McpToolset`, you can pass in an optional `tool_names` filter. This lets you expose only specific functions, like `get_vehicle_valuation`, while hiding others.

Your agent's request is routed through Google Cloud to a dedicated, single-use Vinkius instance running the KBB tools. This instance processes the vehicle data (VINs, models, etc.), gets the valuation, and is terminated immediately after. No data persists on the MCP server.

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