How to Use the RentCast MCP in Google ADK

Connect RentCast to your Google ADK agents to process real estate data within your existing BigQuery and Gemini infrastructure.

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Connect RentCast MCP to Google ADK

Create your Vinkius account to connect RentCast to Google ADK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup RentCast with Google ADK

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Scale market analysis with Google ADK

Feed `list_market_trends` into your Gemini models to analyze regional shifts. Your agents now have direct access to ZIP-level data to inform long-context reasoning. This removes the need for manual data ingestion. You get the trends straight from the source into your Google Cloud agent environment.

Real-time rental comps for agents

Execute `get_rental_comparables` to find similar properties near your target assets. Your agent compares these outputs against your private datasets in BigQuery. This workflow automates the grunt work of market research. You spend less time gathering data and more time reviewing the agent's findings.

Unified valuation tools for Google ADK

Register `get_sale_valuation` as a native tool within your LlmAgent. The agent invokes it whenever it needs a hard number for a property address. This keeps your agent logic tight and prevents hallucinated pricing. You get a clean, data-driven valuation every time you run a query.

Setup guide

Set up RentCast MCP in Google ADK

Prerequisites

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

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with RentCast tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="RentCast_agent",
    model="gemini-2.0-flash",
    instruction="You have access to RentCast tools via MCP.",
    tools=mcp_tools,
)

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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 RentCast. 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

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Common questions about RentCast MCP in Google ADK

You use the McpToolset class to link your agent to the server. It handles the HTTP transport, allowing your agents to call RentCast tools alongside your BigQuery functions.

Yes, they are fully compatible. You can restrict which tools are exposed to your agent using the tool_names filter to keep your agent's scope specific.

You can write custom agent logic that takes the result from `get_property_details` and pushes it to your database. The server provides the data; your agent handles the destination.

The MCP Server throws a standard error that your agent can catch. You should implement retry logic in your Python code to handle transient network issues.

All communication between your agent and the RentCast server is encrypted. Your address-specific data is never cached or used to train external models, keeping your research confidential.

Use it with your favorite AI tools

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