How to Use the Zillow MCP in Pydantic AI

Q: How does Pydantic AI ensure clean Zillow search results?

The searchproperty tool enforces strict data typing. If any field like 'year built' comes back as text instead of an integer, the agent fails loudly and tells you exactly why.

Q: If I need detailed property history on Zillow using Pydantic AI?

The getpropertybyid tool provides all the required data—price history, tax info, photos. The validation ensures you get these fields in a predictable structure.

Q: Does Pydantic AI handle Zillow rental searches correctly?

Yes. When using getrentalproperty, the framework validates that all returned metrics—like rent price and square footage—match their defined types, guaranteeing reliability.

Guarantee structured Zillow data output with Pydantic AI.

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Connect Zillow MCP to Pydantic AI

Create your Vinkius account to connect Zillow 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 Zillow with Pydantic AI

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Type-safe residential search via MCP Server

The `search_property` tool gives you basic property details, including address, price, bedrooms, bathrooms, square footage, lot size, and year built. Critically, every field returned is validated against a strict Pydantic schema. If the underlying API sends unexpected data, your agent won't fail silently; it throws a clear validation error, keeping your workflow accurate.

Validate complex property records with Pydantic AI

Use `get_property_by_id` to pull full details—address, price history, tax info, features, and photos. The power here is that the agent knows exactly what data types to expect for every field. This eliminates guesswork. You get guaranteed correctness when pulling complex records from the MCP Server.

Reliable rental listing retrieval

The `get_rental_property` tool returns necessary rental listings with rent price, bedrooms, bathrooms, square footage, and property type. This data is immediately validated. Your agent can trust that the structure of the output matches your model definition, regardless of how the underlying Zillow system changes.

Setup guide

Set up Zillow 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": {
        "zillow-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Zillow MCP in Pydantic AI

The `search_property` tool enforces strict data typing. If any field like 'year built' comes back as text instead of an integer, the agent fails loudly and tells you exactly why.

The `get_property_by_id` tool provides all the required data—price history, tax info, photos. The validation ensures you get these fields in a predictable structure.

Yes. When using `get_rental_property`, the framework validates that all returned metrics—like rent price and square footage—match their defined types, guaranteeing reliability.

The server touches property details across multiple domains: address, tax info, descriptions, photos, and historical pricing records. All these are typed and validated.

This server touches property details, including address, price history, tax information, descriptions, and photos.

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