How to Use the Zoopla MCP in AutoGen

Force consensus on market insights using AutoGen agents with Zoopla data.

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Connect Zoopla MCP to AutoGen

Create your Vinkius account to connect Zoopla 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 Zoopla with AutoGen

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Debate property value assumptions

Set up an agent to debate the true valuation of an area. One agent can call `zed_index` for a baseline estimate, while another challenges that number by running `average_sold_prices`. The system then converges on a consensus figure. This deliberation is ideal when you need multiple perspectives on one metric. It forces the answer to be vetted against two distinct types of market data.

Challenge listing completeness

Use AutoGen agents to debate whether current listings are exhaustive. One agent pulls `property_listings` for an area, and a second agent challenges that dataset by calling `local_info_graphs` to check historical market activity. The negotiation reveals potential gaps in the data. This multi-agent setup helps surface assumptions—for instance, determining if low sales volume is due to lack of listings or poor local infrastructure scores.

Determine best area for investment

You can build a system where agents debate the best buying opportunity. Agent A checks `property_rich_list` to identify high-value streets, while Agent B uses `local_info_graphs` to verify sustained local development interest. The final conclusion comes from their negotiation. This is superior to simple tool execution because the agents actively weigh competing data points against each other.

Setup guide

Set up Zoopla 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 Zoopla 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="Zoopla_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Zoopla 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="Zoopla_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Zoopla 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 Zoopla. 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 Zoopla MCP in AutoGen

You initiate a conversation between agents. One agent calls `zed_index`, and another challenges it by comparing the result to `average_sold_prices`. The system then outputs a reasoned, agreed-upon value.

Yes. You can set up agents to debate the significance of data retrieved by `local_info_graphs` versus raw listing data from `property_listings`. The debate helps clarify what those stats actually mean for a buyer.

The MCP Server deals only with public property pricing details, specifically the average sold prices. No private personal or owner data is involved in these tools.

It's very helpful. You can assign one agent to find the most valuable areas via `property_rich_list`, and another agent to cross-reference those findings against current available listings.

It excels at them. If a query requires comparing multiple metrics—say, sold prices vs local graphs—you build the system so agents debate which data point is most critical to the final answer.

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