How to Use the Zoopla MCP in Google ADK

Q: How do I get average sold prices using Zoopla and Google ADK?

Simply call averagesoldprices with the area name. The resulting metric is clean data that your Gemini-powered agent can use for complex, multi-step reasoning within your enterprise workflows.

Q: Can my agents build reports using Zoopla and Google ADK?

Yes. Your agent uses propertylistings to get the raw data, then combines it with metrics from localinfographs to create a detailed, fully sourced report in your cloud environment.

Q: What is the process for retrieving Zoopla property value indexes using Google ADK?

You use the specialized zedindex tool. This retrieves the average property value for an area, which then becomes a key input feature when running models in Vertex AI.

Q: If I need specific street comparisons with Zoopla and Google ADK?

Use propertyrichlist to get the most/least expensive streets. Your agent can then cross-reference this against sold prices using averagesoldprices for a full comparison.

Connect Zoopla real estate data into your Google ADK enterprise agents on Vertex AI.

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Connect Zoopla MCP to Google ADK

Create your Vinkius account to connect Zoopla 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 Zoopla with Google ADK

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Process Property Listings with the MCP Server

The `property_listings` tool lets your agent grab a current list of properties for sale or rent in any defined area. Because you're using Google ADK, this data can flow directly into BigQuery tables for deeper analysis. You don't just get the listings; you get structured information that Gemini models can process with their long context window, allowing complex decision-making on property types.

Calculate Area Value Indexes using Zoopla and MCP Server

Need a quick valuation? The `zed_index` tool pulls the Zoopla Zed-Index (average property value) for a specific area. This numeric output is perfect for feeding into Vertex AI models, letting them instantly benchmark market health. Agents also use `average_sold_prices`. By pairing these two tools, you let your system move beyond simple retrieval to comparative analysis of local property worth.

Identify High/Low Value Streets

The `property_rich_list` tool pinpoints the most and least expensive streets in a given area. This is crucial for market research, letting your agent prioritize neighborhoods based on wealth indicators. Additionally, calling `local_info_graphs` gives you URLs to graphs showing local area statistics. You can load these graph links into Gemini's massive context window alongside the street data.

Setup guide

Set up Zoopla 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 Zoopla 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="Zoopla_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Zoopla 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 Zoopla. 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 Zoopla MCP in Google ADK

Simply call `average_sold_prices` with the area name. The resulting metric is clean data that your Gemini-powered agent can use for complex, multi-step reasoning within your enterprise workflows.

Yes. Your agent uses `property_listings` to get the raw data, then combines it with metrics from `local_info_graphs` to create a detailed, fully sourced report in your cloud environment.

You use the specialized `zed_index` tool. This retrieves the average property value for an area, which then becomes a key input feature when running models in Vertex AI.

Use `property_rich_list` to get the most/least expensive streets. Your agent can then cross-reference this against sold prices using `average_sold_prices` for a full comparison.

This server handles real estate market metrics, including active listings, sold price averages, area statistics (via graphs), and property value indices. It’s all quantitative market data.

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