How to Use the Idealista (Real Estate) MCP in LlamaIndex

Q: Can I query Idealista (Real Estate) data semantically using LlamaIndex?

Yes, once you index the outputs of pisosventa or pisosalquiler. Your agent queries the vector store using natural language to find listings that match complex user preferences.

Q: What parameters does the commercial search tool require in this framework?

The localescomerciales tool requires basic location parameters and defaults to rentals. Your LlamaIndex agent can dynamically supply coordinates and price limits based on the user's natural language query.

Index real-time Iberian property listings directly into your LlamaIndex vector stores for RAG queries.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Idealista (Real Estate) MCP to LlamaIndex

Create your Vinkius account to connect Idealista (Real Estate) 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.

GDPR Free for Subscribers

Setup Idealista (Real Estate) with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Index raw property data for semantic RAG

The `buscar_inmuebles` tool retrieves live listings based on precise geographic coordinates and search radii. LlamaIndex takes this raw JSON output, parses the property details, and indexes them directly into your vector store to enable natural language queries over live market data. Let's be real, instead of writing custom scrapers, you run `pisos_venta` to pull active sales listings. Your pipeline embeds these listings instantly, allowing your users to search for quiet apartments near parks using semantic search rather than rigid database filters.

Query commercial trends with LlamaIndex MCP Server tools

The `locales_comerciales` tool fetches active commercial listings, focusing on rentals by default. LlamaIndex indexes these commercial listings alongside residential data pulled from `pisos_alquiler` to build a unified local market knowledge base. Your agent queries this index to find spatial correlations between residential rent prices and commercial availability. This setup grounds your agent's answers in actual, real-time market data, stopping hallucinated property values before they reach your clients.

Index new developments for investment analysis

The `obra_nueva` tool targets newly constructed residential units and off-plan projects in Spain, Italy, and Portugal using this MCP integration. LlamaIndex converts these structured project details into queryable document nodes, making it easy to compare new builds against older resale properties. You can run automated cron jobs that query this tool, update your vector index, and flag new projects that match your investment criteria. Your agent then queries the updated index to generate daily investment briefs.

Setup guide

Set up Idealista (Real Estate) 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 Idealista (Real Estate) 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 Idealista (Real Estate) tools.",
)
response = await agent.run("List recent Idealista (Real Estate) data")

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 Idealista. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Idealista (Real Estate) now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Idealista (Real Estate) MCP in LlamaIndex

Load the tools using the BasicMCPClient and convert them to LlamaIndex tools with McpToolSpec. Your agent runs `buscar_inmuebles` to fetch the data, which you then pass to your indexer.

Yes, once you index the outputs of `pisos_venta` or `pisos_alquiler`. Your agent queries the vector store using natural language to find listings that match complex user preferences.

By grounding your agent's responses directly in the live JSON payloads returned by `buscar_inmuebles`. The agent only references properties that actually exist in the retrieved search results.

The `locales_comerciales` tool requires basic location parameters and defaults to rentals. Your LlamaIndex agent can dynamically supply coordinates and price limits based on the user's natural language query.

All GPS coordinates and search queries pass through a zero-trust, ephemeral V8 sandbox. Your location data is never written to persistent logs or used to train models.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript