How to Use the Nestoria MCP in Google ADK

Q: How do I register the Nestoria toolset within my Google ADK agent?

Use the McpToolset class with your HTTP server parameters. Pass that toolset into your LlmAgent configuration, and Gemini will immediately start using searchlistings for property queries.

Connect Gemini agents to global real estate markets with Nestoria and the Google ADK.

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Connect Nestoria MCP to Google ADK

Create your Vinkius account to connect Nestoria to Google ADK 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 Nestoria with Google ADK

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Feed Live Property Data to Google ADK Gemini Models

Give your long-context Gemini models access to actual real estate listings. By connecting this MCP Server, your Google ADK agent can call `search_listings` to pull live pricing and availability into its 1M+ token context window. This lets your agent analyze massive lists of properties against complex user requirements. You can feed a client's entire life history from BigQuery and let Gemini find the perfect match.

Build Enterprise Real Estate Agents on Google Cloud

Stop writing fragile glue code to connect real estate APIs to Vertex AI. This integration exposes `search_listings` as a native tool inside your Google ADK pipeline, handling the MCP Server transport under the hood. Your enterprise agents can now cross-reference internal customer databases with live market listings. It brings global property search directly into your secure Google Cloud environment.

Restrict Exposed Tools to Keep Your Agents Focused

Keep your Gemini models from getting distracted by unused tools. The Google ADK lets you apply a tool name filter when you register this MCP Server, ensuring your agent only sees `search_listings` and nothing else. This restriction reduces model confusion and keeps latency low. Your agent won't waste time trying to decide between multiple overlapping tools when looking up homes.

Setup guide

Set up Nestoria 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 Nestoria 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="Nestoria_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Nestoria tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Nestoria MCP in Google ADK

Use the `McpToolset` class with your HTTP server parameters. Pass that toolset into your `LlmAgent` configuration, and Gemini will immediately start using `search_listings` for property queries.

Absolutely. Your Google ADK agent can fetch raw property listings via `search_listings` and then pass that structured data directly to your BigQuery ingestion pipelines for long-term analysis.

Yes. The Google ADK accommodates both transport types, allowing you to connect to this MCP Server over standard input/output for local testing or HTTP for cloud deployments.

You should implement throttling at the agent level or within your Google Cloud infrastructure. Because `search_listings` queries live endpoints, managing the frequency of agent calls prevents API blocks.

Every search query containing location preferences or budget constraints is routed through zero-trust, ephemeral isolates. No search data is cached on disk, ensuring your clients' house-hunting parameters remain private.

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