AlterLab

Posted on Jun 28 • Originally published at alterlab.io

How to Give Your AI Agent Access to Zillow Data

#aiagents #dataextraction #api #mcp

This guide covers accessing publicly available data. Always review a site's robots.txt and Terms of Service before automated access.

TL;DR

Give your AI agent access to Zillow data by calling AlterLab’s Extract API with a URL and a JSON schema. The API returns clean, structured JSON ready for LLM context, handling JavaScript, anti‑bot measures, and proxies automatically. Use the Search API for query‑based retrieval and the MCP server to expose AlterLab as a tool for Claude, GPT, or Cursor agents.

Why AI agents need Zillow data

AI agents working in real‑estate, finance, or consumer‑research pipelines require fresh, structured property information. Three common agentic use cases are:

Property valuation pipelines – an agent pulls recent sale prices, tax assessments, and home characteristics to run comparative market analysis for a LLM‑driven appraisal model.
Market trend monitoring – a scheduled agent scrapes listing counts, median prices, and inventory changes across zip codes to feed a trading‑signal generator.
Investment research – an agent collects rental yield estimates, renovation cost indicators, and school‑district data to enrich a RAG knowledge base for portfolio decisions.

In each case the agent needs reliable, machine‑readable data without spending tokens on failed requests or HTML parsing.

Why raw HTTP requests fail for agents

Direct HTTP requests to Zillow quickly run into obstacles that waste an agent’s compute and token budget:

Rate limiting – Zillow enforces per‑IP request caps; a naïve agent will see HTTP 429 responses and must implement back‑off logic.
JavaScript rendering – many property pages load data client‑side; raw HTML returns shells with no usable content.
Bot detection – headless‑browser fingerprints trigger CAPTCHAs or outright blocks, forcing manual intervention.
Token waste – failed requests consume API calls and LLM context windows with error messages instead of useful data.

These patterns force engineers to write fragile scraping loops that distract from the core agent logic.

Connecting your agent to Zillow via AlterLab

AlterLab’s Extract API (/api/v1/extract) returns structured data directly, eliminating the need for custom parsers. The API handles headless Chrome, proxy rotation, and anti‑bot bypass under the hood.

Python example

```python title="agent_zillow_extract.py" {3-8}

client = alterlab.Client("YOUR_API_KEY")

Ask for title, price, and description from a Zillow listing

result = client.extract(
url="https://www.zillow.com/homedetails/123-Main-St-Seattle-WA-98101/20567890_zpid/",
schema={
"title": "string",
"price": "string",
"description": "string",
"bedrooms": "integer",
"bathrooms": "number",
"zipcode": "string"
}
)

result.data is a plain dict ready for your LLM

print(result.data)




### cURL equivalent


```bash title="Terminal"
curl -X POST https://api.alterlab.io/api/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://www.zillow.com/homedetails/123-Main-St-Seattle-WA-98101/20567890_zpid/",
    "schema": {
      "title": "string",
      "price": "string",
      "description": "string",
      "bedrooms": "integer",
      "bathrooms": "number",
      "zipcode": "string"
    }
  }'

If you need the raw HTML (e.g., to feed a vision model), use the Scrape API (/api/v1/scrape) with the same authentication.

When to choose Scrape over Extract

Use Extract when you want predefined fields (price, address, etc.) as JSON.
Use Scrape when you need the full rendered page for downstream processing or visual analysis.

Both endpoints accept parameters like min_tier to force a specific rendering level and formats to request JSON, Markdown, or plain text output.

Using the Search API for Zillow queries

Agents often need to discover listings based on criteria rather than a known URL. AlterLab’s Search API (/api/v1/search) lets you pass a free‑text query and receive structured results.

```python title="agent_zillow_search.py" {3-7}

client = alterlab.Client("YOUR_API_KEY")

Search for 3‑bed homes under $800k in Seattle

search_result = client.search(
query="Seattle WA 3 bedrooms under 800000",
limit=10
)

for item in search_result.data:
print(item["url"], item["price"])




The Search API internally forwards the query to Zillow, renders the results page, extracts the listing cards, and returns each as a structured object. This pattern is ideal for agents building dynamic property‑sourcing pipelines.

## MCP integration
AlterLab provides an MCP (Model‑Context‑Protocol) server that exposes the Extract and Search APIs as tools compatible with Claude Desktop, GPT‑function‑calling, or Cursor. Agents can call `alterlab_extract` or `alterlab_search` just like any other tool, receiving structured data directly in the LLM’s context window.

See the full walkthrough: [AlterLab for AI Agents](https://alterlab.io/docs/tutorials/ai-agent).

## Building a property valuation pipeline
Below is an end‑to‑end example showing how an AI agent can produce a valuation estimate using live Zillow data.

### Step 1 – Define the agent’s goal
The agent receives a user request: “Estimate the market value of 456 Oak Ave, Portland, OR.”

### Step 2 – Locate the property via Search


```python title="pipeline_step1.py" {3-6}
search = client.search(
    query="456 Oak Ave Portland OR",
    limit=1
)
property_url = search.data[0]["url"]

Step 3 – Extract structured fields

```python title="pipeline_step2.py" {3-9}
data = client.extract(
url=property_url,
schema={
"price": "string",
"bedrooms": "integer",
"bathrooms": "number",
"sqft": "integer",
"year_built": "integer",
"zipcode": "string"
}
)




### Step 4 – Feed the LLM with clean data


```python title="pipeline_step3.py" {3-6}
prompt = f"""
You are a real‑estate analyst. Using the following property details, provide a concise market‑value estimate and brief rationale.

{data.data}
"""
valuation = llm.generate(prompt)
print(valuation)

Step 5 – Return the answer to the user

The agent sends the LLM’s output back to the user, completing the tool call. No HTML parsing, no retry loops, and the agent’s token budget is spent solely on reasoning.

Infographic: Steps in the pipeline

Infographic: Performance stats

Key takeaways

Use AlterLab’s Extract API to turn any Zillow page into ready‑to‑use JSON for your AI agent.
The Search API enables query‑driven discovery without hard‑coding URLs.
MCP lets you expose AlterLab as a first‑class tool for LLM‑based agents, reducing glue code.
By offloading JavaScript rendering, proxy management, and anti‑bot bypass to AlterLab, your agent spends tokens on analysis, not on overcoming access barriers.
Review the pricing page to estimate costs for your expected request volume.

Try it yourself: extract live Zillow data for your agent.

AlterLab // Web Data, Simplified.

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