How to Use the Zoopla MCP in OpenAI Agents SDK

Q: How do I check average sold prices using Zoopla and OpenAI Agents SDK?

You call the averagesoldprices tool and specify the target area. The agent executes this, returning a clean data point that your client can immediately use in its response or further calculations.

Q: Can I get local graphs for Zoopla with OpenAI Agents SDK?

Absolutely. Just invoke localinfographs. This tool returns the exact URLs you need to show statistical breakdowns of an area, letting your agent present data that's ready for consumption.

Q: What is the best way to find property listings with Zoopla and OpenAI Agents SDK?

The propertylistings tool handles this. You pass the desired search area, and your agent gets back a comprehensive list of active properties for both sale and rent. It's structured data ready for display.

Q: Does Zoopla support finding property value indexes with OpenAI Agents SDK?

Yes, the zedindex tool provides the current Zoopla Zed-Index (average property value) for any specified area. This is a quick way to benchmark neighborhood worth.

Connect Zoopla real estate data directly into your OpenAI Agents SDK production workflows.

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Connect Zoopla MCP to OpenAI Agents SDK

Create your Vinkius account to connect Zoopla 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 Zoopla with OpenAI Agents SDK

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Analyze Property Data with the MCP Server

The `property_listings` tool gets you a fresh list of properties for sale or rent in any given area. Your agent uses this to build targeted marketing campaigns, pulling specific details like bedrooms and price ranges. The system also lets you use `zed_index`. This instantly provides the Zoopla Zed-Index—the average property value—for an area, helping your agents quickly decide if a neighborhood is worth focusing on.

Graph Local Area Statistics with OpenAI Agents SDK

Need visuals? The `local_info_graphs` tool retrieves URLs for graphs showing local area statistics. Your agent can then pass these specific links to other services or directly present them in a report, giving users instant context. This capability works alongside the `average_sold_prices` tool. By combining sold price data with visual stats, you let your AI client build comprehensive market reports without needing manual API calls.

Compare Property Value Hotspots

Use the `property_rich_list` tool to identify the most expensive and least expensive streets in a given area. This is perfect for real estate investors who need to pinpoint value discrepancies quickly. Complementing this, the `average_sold_prices` function lets you retrieve concrete sold price data for that same area. You can now give your agent specific numbers—not just generalizations—to back up its recommendations.

Setup guide

Set up Zoopla 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 Zoopla tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Zoopla 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="Zoopla Agent",
            instructions="You have access to Zoopla tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Zoopla MCP in OpenAI Agents SDK

You call the `average_sold_prices` tool and specify the target area. The agent executes this, returning a clean data point that your client can immediately use in its response or further calculations.

Absolutely. Just invoke `local_info_graphs`. This tool returns the exact URLs you need to show statistical breakdowns of an area, letting your agent present data that's ready for consumption.

The `property_listings` tool handles this. You pass the desired search area, and your agent gets back a comprehensive list of active properties for both sale and rent. It's structured data ready for display.

Yes, the `zed_index` tool provides the current Zoopla Zed-Index (average property value) for any specified area. This is a quick way to benchmark neighborhood worth.

This server touches real estate metrics: listing status, sold prices, average property values (Zed-Index), and local area statistics. It’s all market data.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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