How to Use the Zillow MCP in LlamaIndex

Q: How can LlamaIndex help me query past Zillow searches?

LlamaIndex indexes the results of your searchproperty calls into a vector store. You can then ask questions about those old sessions—like 'What were the average prices for homes built before 1980 in this area?'

Build knowledge-augmented RAG applications with LlamaIndex and MCP Server.

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Connect Zillow MCP to LlamaIndex

Create your Vinkius account to connect Zillow to LlamaIndex 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 LlamaIndex

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Indexing Zillow Search Results via LlamaIndex

When you run `search_property`, the results are indexed by your LlamaIndex setup. This means the property details, like lot size or year built, aren't just passed through; they become part of a searchable knowledge base. Later on, you can query this index about past sessions—for instance, asking, 'What was the average square footage in zip code 90210 last month?' and getting an answer grounded directly in the API data.

Semantic Search for Property Data with LlamaIndex

LlamaIndex makes the `get_property_by_id` output semantically searchable. Instead of just needing a specific ID, you can ask a question like, 'Show me properties that have high tax info and are over 2000 sq ft.' The system finds relevant records even if your query doesn't match exact field names. It treats the raw data—the description, photos metadata, price history—as text chunks in an index. This is a huge jump from simple database lookups.

Creating Unified Zillow RAG Apps with LlamaIndex

You can combine live API calls and documents into one unified knowledge base using this MCP Server. For example, you write a document about neighborhood zoning laws, and then index the results from `get_rental_property` alongside it. The resulting RAG application lets your agent answer questions by cross-referencing the legal text with the actual rental listing data, preventing hallucinations.

Setup guide

Set up Zillow MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Zillow MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Zillow tools.",
)
response = await agent.run("List recent Zillow data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Zillow. 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.

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Common questions about Zillow MCP in LlamaIndex

LlamaIndex indexes the results of your `search_property` calls into a vector store. You can then ask questions about those old sessions—like 'What were the average prices for homes built before 1980 in this area?'

Yes. The MCP Server output, which includes full Property details and price history, becomes part of your searchable index. You can query metadata like tax info or features semantically.

Build a RAG application that combines general documents (like market reports) with live API calls, such as those from `get_property_by_id`, so your agent always has grounded context.

It significantly. By indexing the actual results rather than just passing them through, you ground your AI's answers in verifiable API output, eliminating guesswork and hallucinations.

This MCP Server touches Property details and price history. When setting up the index, remember that all stored results are part of your knowledge base, so manage access controls carefully.

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