Zoopla MCP for AI. Analyze UK market trends and property values in minutes.
Works with every AI agent you already use
…and any MCP-compatible client








How this MCP server connects to your AI agent
Zoopla connects your AI agent directly to UK property market data. You can search current listings for sale or rent, check average sold prices in any area, find local statistics like crime rates and education scores, or determine a neighborhood's general value using the Zed-Index.
What AI agents can do with Zoopla Automation
Average sold prices
Gets the average amount paid for properties sold in a specific area.
Local info graphs
Retrieves links to graphs that show local statistics like crime and schooling levels.
Property listings
Gets a list of properties currently available for sale or rent in an area.
Find available houses and flats for sale or rent in specific UK areas.
Determine the typical worth of a home in an area using specialized market indices.
Retrieve data on what similar properties sold for over time to gauge market movement.
Get URLs pointing to local statistics, including crime rates and educational metrics.
Pinpoint the most expensive and least expensive streets within a defined area.
Ask an AI about this
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What AI agents can do with Zoopla MCP: 5 Tools for Real Estate Data
Analyze sold prices, retrieve current listings, and assess local demographics using these five specialized tools.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Zoopla on VinkiusAverage Sold Prices
Gets the average amount paid for properties sold in a specific area.
Local Info Graphs
Retrieves links to graphs that show local statistics like crime and schooling levels.
Property Listings
Gets a list of properties currently available for sale or rent in an area.
Property Rich List
Identifies the most and least expensive streets within a given neighborhood.
Zed Index
Retrieves the overall average property value index for a target area.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Zoopla, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
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.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Built on the Model Context Protocol (MCP) for Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 5 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The Headache of Manual Market Research, Solved with Vinkius AI Gateway
Today, researching a market feels like juggling five different websites. You open one site for available properties, another for historical data, and yet a third just for local crime stats. You spend hours copy-pasting addresses into spreadsheets, then manually cross-referencing sold prices against index graphs just to build one decent report.
With this MCP, that manual labor disappears. You talk to your agent: 'Give me the full picture of SW1.' The system pulls together current property listings, overlays historical average sold prices, and drops local info graph links—all in a single conversation. What you get is an immediate, multi-layered market briefing.
Property Listings
Instead of clicking through dozens of search filters across multiple tabs and hoping you don't miss the one perfect house, your agent runs the property_listings tool. It handles all those complex boolean searches—price range AND minimum bedrooms AND specific type—instantly.
It’s simple: You tell it what you want, and it brings back only what matches. No endless scrolling through irrelevant results.
What your AI can actually do with this
Need to evaluate a potential investment? Zoopla lets your AI client pull together a full picture of any UK location. You can start by asking for current listings, filtering properties instantly by price range or number of bedrooms. To validate that area, you can check historical average sold prices and get the current Zed-Index value for instant context.
For deeper due diligence, the system pulls local info graphs showing demographics, crime rates, and education levels right into your chat window. Plus, it pinpoints the most expensive and least expensive streets in a given area. Because property data requires handling sensitive keys—and you don't want those keys sitting on a disk—Vinkius handles all credential passage through a zero-trust proxy.
This means you get the full market picture without ever compromising security.
019e5d6b-ec0b-72bd-af28-f08528a041e9 Here's how it actually works
The bottom line is you get complex, multi-layered market reports from one conversational prompt.
First, connect your API key to the Vinkius platform. You'll select Zoopla as one of the available MCPs.
Next, you simply ask your agent a question—for instance, 'What are the average sold prices in SW1?' Your AI client handles the rest, running the necessary tool calls.
Finally, the system returns clean, contextual data directly to your chat window, whether that's a list of properties or a graph URL.
Who is this actually for?
This is for the real estate investor who needs to move fast and validate a deal before showing up at a viewing. It's also for the data analyst drowning in disparate spreadsheets trying to build market reports from raw public domain data.
Needs to quickly analyze average sold prices and local trends across multiple outcodes to find high-yield buying opportunities.
Wants to filter property listings by specific criteria while simultaneously checking the neighborhood's educational status or crime profile.
Extracts property rich list data and Zed-Index values for quarterly market reporting and competitive analysis.
What Changes When You Connect
Go beyond simple searches. You can validate a location's true worth by cross-referencing the Zed-Index with historical average sold prices, giving you a much fuller picture than any single listing page provides.
Stop guessing about a neighborhood. By pulling local info graphs, you get objective data on crime rates and education levels—essential context for both buyers and investors.
Need to narrow your focus? Use property_listings to filter through hundreds of available properties based on exact criteria (beds, price) without leaving the chat interface.
Understand market movement by checking average sold prices. This tool lets you spot if a neighborhood is steadily appreciating or plateauing over time.
Pinpoint prime real estate zones using the property_rich_list to see which streets command the highest premiums and where value might be dropping off.
See it in action
Evaluating a new investment zone
A developer needs to check out three potential sites. They ask their agent to run average_sold_prices for all three areas, then use the zed_index on each one, and finally get local_info_graphs to compare demographic stability before making a site selection.
Finding a first family home
A buyer is interested in an area but isn't sure if it’s right. They use property_listings to see what’s available, then ask for the zed_index and local info graphs to ensure the crime rate and school quality meet their standards.
Building a comparative market report
A data analyst needs a report on high-value areas. They use property_rich_list to find the top streets, then run average_sold_prices for those streets over the last year, compiling all data points into one document.
Validating listing pricing
A user finds a unique listing price. They ask their agent to check that area's zed_index and run property_listings for similar homes just before or after the listed date to see if the asking price is in line with market reality.
The honest tradeoffs
Using listings alone
Just scrolling through available properties without context. You might find a beautiful house, but you'll have no idea if the street is prone to flooding or if the area’s value is dropping.
Always validate that listing data by running local_info_graphs and checking the zed_index. That gives you the required background context.
Mixing up historical and current data
Assuming because a street was rich last year, it's still top tier today. The market changes fast, and old sales figures don't tell the whole story.
Cross-reference average_sold_prices with property_listings. This lets you see both historical trends and what's actually on the market right now.
Forgetting to check boundaries
Focusing only on one small area and missing out on neighboring opportunities that might be undervalued.
Run zed_index on multiple adjacent areas. Use the local_info_graphs comparison feature to see if a nearby neighborhood has better educational scores or crime stats.
When It Fits, When It Doesn't
Use this MCP when you need more than just an address and a price tag. If your goal is simple—'Show me all available houses under £300k in Manchester'—then the property_listings tool handles it fine. But if you need to know why those houses are priced that way, or if the entire neighborhood is trending up or down over five years, then this MCP is required. Don't rely on just one data point; combine local info graphs with average_sold_prices for a full picture. If you only care about finding the absolute highest-end street and nothing else, property_rich_list works alone. But if you need to validate that high-end value against current listings or recent sales, then bring in zed_index.
Questions you might have
How do I use the zed_index tool with property_listings? +
You run them sequentially. First, find a neighborhood you like using property_listings for general context. Then, immediately follow up by asking for the zed_index of that specific area to validate its overall market value.
Can I compare different neighborhoods’ local info graphs? +
Yes. You can ask your agent to retrieve local_info_graphs URLs for two or three distinct areas in one go, letting you easily compare crime rates or educational metrics side-by-side.
Which tool should I use if I only want sold prices? +
Use average_sold_prices. This specific tool pulls the historical sales data for a given area, letting you see what people actually paid over time.
Is this MCP better than just using Google Maps? +
It's much more precise. While maps show location, this MCP provides verifiable market metrics like property_rich_list data and statistically backed local info graphs that are impossible to find in a simple map search.
When using `property_rich_list`, how does Vinkius handle my Zoopla API Key? +
The system uses a zero-trust proxy for all key usage. Your credentials pass through in transit but never get stored on disk, keeping your data secure.
If I need to analyze many areas using `zed_index`, are there rate limits or performance considerations? +
The MCP handles connection throttling and manages calls efficiently. If you run too many queries in quick succession, the system will pause and notify your agent.
Does calling `local_info_graphs` provide raw data or just image URLs for local statistics? +
It provides direct URL endpoints to visual data graphs. Your AI client can then fetch these links and present the associated neighborhood metrics.
If I run `average_sold_prices` using a very large or ambiguous postcode, how does it report results? +
The MCP is designed to pinpoint the best match for the provided area. If ambiguity exists, it will return all possible matches and ask your agent which one you want.
Can I find the average property value for a specific UK postcode? +
Yes. You can use the zed_index tool by providing the area name and setting the output_type to 'outcode' to get the average property value (Zed-Index) for that location.
How do I see which streets are the most expensive in a particular town? +
Use the property_rich_list tool. Simply provide the area name, and the agent will return a list of the most and least expensive streets in that area based on Zoopla data.
Can I filter property searches by price range and number of bedrooms? +
Absolutely. The property_listings tool allows you to specify minimum_price, maximum_price, minimum_beds, and maximum_beds to narrow down your search results.
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