How to Use the Zoopla MCP in Windsurf

Q: How can I find average sold prices with Zoopla and Windsurf?

Just call averagesoldprices. It fetches the actual mean sold price for a given area, making your reports grounded in historical transaction data. You don't just get listings; you get proof of past sales.

Q: Does Zoopla and Windsurf support searching multiple property types?

Yes. The propertylistings tool retrieves a full list of properties—whether they are for sale or rent—in the area you specify. You can process both sides of the market with one call.

Q: Can I get the most expensive streets using Zoopla and Windsurf?

Absolutely. The propertyrichlist tool handles this. It scans an area and gives you a breakdown of both the highest-value and lowest-value streets, letting your agent build out detailed comparative analysis.

Analyze UK property markets with Windsurf. Get market insights without writing a line of code.

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Connect Zoopla MCP to Windsurf

Create your Vinkius account to connect Zoopla to Windsurf 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 Windsurf

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Windsurf: Find Local Area Stats

Use `local_info_graphs` to pull URLs for local area statistics graphs. This shows the Windsurf user exactly what kind of market data is available in a neighborhood. It's perfect for reports that need visual proof points, giving you immediate links to detailed charts about any given location.

MCP Server: Check Property Values

The `zed_index` tool lets your agent pull the Zoopla Zed-Index—the average property value—for a specific area. You get a single, definitive metric that defines the market health of that location. It's faster than calling multiple APIs because it gives you one core figure: the overall valuation for the zip code.

Windsurf: Compare Street Costs

Run `property_rich_list` to quickly determine the most and least expensive streets in an area. This tool doesn't just list properties; it profiles street-level value differences. It’s a great way to segment market reports, showing clients exactly where the money is flowing by comparing high-end vs. budget areas.

Setup guide

Set up Zoopla MCP in Windsurf

Prerequisites

Windsurf IDE installed (macOS, Windows, or Linux)
Active Vinkius subscription with a valid endpoint token

1

Open MCP configuration

Click the Cascade assistant icon in the sidebar, then click the hammer icon (🔨) at the top of the panel. Select "Configure" to open ~/.codeium/windsurf/mcp_config.json.
2

Add the Zoopla MCP

Paste the JSON snippet shown on the right into the mcpServers object. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Refresh MCPs

Go back to the hammer icon (🔨) in Cascade and click "Refresh". Windsurf will detect the new server. No full restart is needed — the connection is hot-reloaded.
4

Verify in Cascade

Start a new Cascade conversation and ask something like "Show my Zoopla payment history." If connected, Cascade will call the Zoopla tools directly. You will see a green dot next to the server name in the MCP panel.

mcp_config.json

{
  "mcpServers": {
    "zoopla-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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Common questions about Zoopla MCP in Windsurf

Just call `average_sold_prices`. It fetches the actual mean sold price for a given area, making your reports grounded in historical transaction data. You don't just get listings; you get proof of past sales.

Yes. The `property_listings` tool retrieves a full list of properties—whether they are for sale or rent—in the area you specify. You can process both sides of the market with one call.

This MCP Server touches property listing data, specifically records that include sale or rental status. We only provide aggregated insights, never private buyer or seller information.

Absolutely. The `property_rich_list` tool handles this. It scans an area and gives you a breakdown of both the highest-value and lowest-value streets, letting your agent build out detailed comparative analysis.

The tools are designed to be efficient. For instance, getting a single area's average value via `zed_index` is quick enough that your autonomous agent can chain it into multi-step reports without delay.

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