How to Use the Zillow MCP in AutoGen

Q: How does the MCP Server support complex decision-making with AutoGen?

The server provides structured tools (getpropertybyid, etc.) that agents can call. The power comes from having multiple agents debate which tool to call, and in what sequence, based on their unique roles.

Design consensus-driven decision systems for AutoGen agents using MCP Server tools.

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Connect Zillow MCP to AutoGen

Create your Vinkius account to connect Zillow to AutoGen 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 AutoGen

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Debating Property Valuation with AutoGen

You can set up multiple AutoGen agents to debate the best valuation for a home. One agent uses `get_property_by_id` to pull tax info and price history, while another focuses on the structural features listed in the description. The agents talk through these findings until they reach a consensus decision on a value range. It's not just running a tool; it’s making competing perspectives negotiate an answer.

Negotiating Best Rental Options via AutoGen

Use the MCP Server to let agents compare results from `search_property` and `get_rental_property`. One agent might argue for a location based on low rent prices, while another pushes back by citing poor lot size metrics. They challenge each other until they pick the best fit. This debate structure is ideal when the right answer depends on balancing competing criteria—like cost versus features.

Multi-Agent Zillow Research with AutoGen

The system needs to check both residential and rental markets. You can assign one agent to execute `search_property` for residences, and a second agent to run `get_rental_property` for rentals. The agents then debate which market segment should be prioritized based on the user's prompt. The result is a decision that required deliberation between two different functional viewpoints.

Setup guide

Set up Zillow MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Zillow tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Zillow_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Zillow data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Zillow_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Zillow data")
    print(result.messages[-1].content)

Get your connection token →

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 AutoGen

AutoGen doesn't just report data; it forces agents to debate the implications of the data. For example, one agent might flag a property due to old tax info from `get_property_by_id`, and another might argue that its features outweigh the risk.

Yes. You can create multi-agent workflows where one agent searches for properties, a second analyzes the price history, and a third synthesizes them into a final recommendation.

Data like property type, bedrooms, bathrooms, and listing status work well. The agents can challenge each other on whether one set of metrics is more important than another.

The server provides structured tools (`get_property_by_id`, etc.) that agents can call. The power comes from having multiple agents debate which tool to call, and in what sequence, based on their unique roles.

This MCP Server touches Property details and price history. When designing your multi-agent system, ensure the debate process does not expose sensitive raw identifiers or personal user inputs.

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