How to Use the Zoopla MCP in LlamaIndex

Q: How can LlamaIndex help me analyze Zoopla property listings?

You pass the output of propertylistings to your FunctionAgent. This indexes the listing data, allowing you to perform semantic searches across thousands of records without needing complex filtering logic.

Q: Can I use LlamaIndex for historical Zoopla sold price trends?

Yes. By indexing results from averagesoldprices, you create a searchable timeline of property value changes. You can query past data points to track market movement.

Q: Is using propertyrichlist effective with LlamaIndex?

Absolutely. Indexing the results from propertyrichlist lets you build a searchable profile of valuable streets, which is great for comparative analysis across different regions.

Q: Does LlamaIndex support advanced querying on Zoopla data?

Yes. You can combine the results from multiple tools—like zedindex and localinfographs—and index them together, creating a unified knowledge base for complex queries.

Index Zoopla property data into a searchable knowledge base using LlamaIndex.

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Connect Zoopla MCP to LlamaIndex

Create your Vinkius account to connect Zoopla 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 Zoopla with LlamaIndex

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Use `zed_index` to pull the average property value for an area. When you index this result, your AI client stores it as a verifiable fact in the knowledge base. Later queries can then be grounded by retrieving this specific sold price metric. If you run `property_listings`, that raw data gets indexed too. Instead of hallucinating general market trends, your agent answers using the actual listing records saved to your vector store.

Search local stats and listings

You can query historical context about a neighborhood by indexing results from `local_info_graphs`. This allows you to search past sessions—like 'What were the local graph URLs for this area last week?' Furthermore, querying indexed data from `property_listings` means your agent can pull up specific property details based on natural language queries against the saved API output.

Analyze sold price history in index

Run `average_sold_prices` to get a current average. Indexing this result allows you to build an historical record of market fluctuations. You can query across time to see how the 'average sold prices' metric has changed for a given area. This mechanism lets you combine static documents with live API data, making your knowledge index far more powerful than simple document retrieval.

Setup guide

Set up Zoopla 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 Zoopla 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 Zoopla tools.",
)
response = await agent.run("List recent Zoopla 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 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 LlamaIndex

You pass the output of `property_listings` to your FunctionAgent. This indexes the listing data, allowing you to perform semantic searches across thousands of records without needing complex filtering logic.

Yes. By indexing results from `average_sold_prices`, you create a searchable timeline of property value changes. You can query past data points to track market movement.

This server touches public property pricing data, specifically the average sold prices and listing details. The indexed data remains within your secure knowledge base.

Absolutely. Indexing the results from `property_rich_list` lets you build a searchable profile of valuable streets, which is great for comparative analysis across different regions.

Yes. You can combine the results from multiple tools—like `zed_index` and `local_info_graphs`—and index them together, creating a unified knowledge base for complex queries.

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