How to Use the Zoopla MCP in Pydantic AI

Q: How do I get property listings using Zoopla and Pydantic AI?

You invoke propertylistings, providing the area. The agent handles this via a type-safe flow, ensuring that every listing field—like price or size—is correctly validated against your defined model.

Q: Can I get average sold prices using Zoopla and Pydantic AI?

Yes. The averagesoldprices tool returns a metric that is immediately passed through Pydantic validation. This prevents the agent from acting on unexpected or malformed price data.

Q: What's the best way to use Zoopla for valuation indexes with Pydantic AI?

Use the zedindex tool. Since your client cares about correctness, Pydantic guarantees that the resulting average property value adheres strictly to its defined data type.

Q: Does Zoopla support analyzing multiple street values with Pydantic AI?

The propertyrichlist tool identifies expensive and cheap streets. Your agent can then validate this list, ensuring that every returned street name and associated metric is exactly as expected.

Use Zoopla real estate data with Pydantic AI for type-safe, validated Python agents.

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Create your Vinkius account to connect Zoopla to Pydantic AI 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 Pydantic AI

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Retrieve and Validate Property Listings

The `property_listings` tool provides a list of properties for sale or rent in a defined area. When using Pydantic AI, the agent validates every returned field against your schema. This means if the API sends bad data, your agent fails loud—no corrupted fields. This type-safety is critical when building mission-critical agents that rely on correct real estate details.

Analyze Area Metrics with Pydantic AI

The `zed_index` tool retrieves the Zoopla Zed-Index, giving a validated average property value for an area. Because of Pydantic's validation layer, you know exactly what numeric type and range that index will be in. Similarly, calling `average_sold_prices` ensures that the sold price data you receive is correctly typed before your agent processes it.

Map Local Area Statistics via MCP Server

Need visual proof? The `local_info_graphs` tool returns URLs for graphs showing local area statistics. Your agent treats these URLs as validated strings, making them reliable inputs for other services. Furthermore, combining this with the street-level data from `property_rich_list` allows your agent to generate highly structured reports where every piece of information is guaranteed correct.

Setup guide

Set up Zoopla MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "zoopla-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Zoopla tools.",
)

result = await agent.run("List recent Zoopla transactions")
print(result.output)

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Common questions about Zoopla MCP in Pydantic AI

You invoke `property_listings`, providing the area. The agent handles this via a type-safe flow, ensuring that every listing field—like price or size—is correctly validated against your defined model.

Yes. The `average_sold_prices` tool returns a metric that is immediately passed through Pydantic validation. This prevents the agent from acting on unexpected or malformed price data.

Use the `zed_index` tool. Since your client cares about correctness, Pydantic guarantees that the resulting average property value adheres strictly to its defined data type.

The `property_rich_list` tool identifies expensive and cheap streets. Your agent can then validate this list, ensuring that every returned street name and associated metric is exactly as expected.

This server provides structured real estate market data: active listings, sold prices, area statistics (graphs), and property value indices. The goal is to make sure all these fields are type-safe.

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