How to Use the Zoopla MCP in CrewAI

Run autonomous Zoopla operations using CrewAI multi-agent teams.

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Connect Zoopla MCP to CrewAI

Create your Vinkius account to connect Zoopla to CrewAI 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 CrewAI

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Perplexity

Coordinate Listing Research Across Agents

The `property_listings` tool allows Agent A to research properties for sale or rent in a given area. This data is then passed to Agent B, which analyzes the listings and compiles a summary report. CrewAI's shared memory ensures that every agent—from the researcher to the analyst—is working with the same, consistent set of property records.

Analyze Area Value With Dedicated Agents

Agent A can use `zed_index` to retrieve the Zoopla Zed-Index for a target area. Agent B then takes that index value and uses it in conjunction with data from `average_sold_prices`. This separation of concerns makes complex analysis manageable. The monitor agent watches this entire session, ensuring all steps—from initial research to final calculation—are completed successfully.

Build Comprehensive Local Market Reports

A specialized 'Reporting Agent' can use `local_info_graphs` to fetch URLs and then compile them into a single, coherent output. This tool gathers necessary local statistics that multiple agents need for their final report. Using the CrewAI framework means you are building autonomous operations; no human intervention is required after setting up the agent roles.

Setup guide

Set up Zoopla MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Zoopla tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Zoopla Analyst",
    goal="Access and analyze Zoopla data via MCP.",
    backstory="Expert analyst with direct Zoopla access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Zoopla transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

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

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Zoopla Analyst",
        goal="Access and analyze Zoopla data via MCP.",
        backstory="Expert analyst with direct Zoopla access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Zoopla transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

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 CrewAI

You assign specialized roles. One agent researches the data (using `property_listings`), another analyzes it (using `zed_index`), and a third compiles the final report. The system runs them sequentially or hierarchically.

You don't run one agent; you run a specialized team. This gives you role-based specialization and shared memory, which prevents data silos when analyzing complex real estate markets.

You can build full cycles: Agent 1 gathers raw property listings, Agent 2 checks the historical sold prices, and a Moderator Agent finalizes a comprehensive summary report.

The server touches multiple real estate data types: current property listings, local statistics URLs, and historical average sold prices for UK regions.

You pass the MCP Server URL directly in your agent definitions. For advanced setups, you can use `tool_filter` to ensure only the specific Zoopla tools required for a job are exposed.

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