How to Use the Bridge Data Output MCP in Google ADK

Q: How do we combine Bridge Data Output and Google ADK for market analysis?

Your agent queries active inventory using searchpropertiesbyprice, then cross-references the results with historical sales data stored in BigQuery. This setup lets you build automated valuation models using real-time MLS inputs.

Q: Does this MCP Server handle high-resolution property media?

It does. The listmedia tool retrieves photo and video metadata for listings, which your agent can process or send to Gemini Multimodal models for visual analysis.

Feed real-time MLS listings directly into your Google ADK pipelines using this MCP Server to analyze massive property datasets with Gemini.

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Connect Bridge Data Output MCP to Google ADK

Create your Vinkius account to connect Bridge Data Output 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 Bridge Data Output with Google ADK

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Query real estate endpoints with Google ADK tools

The Bridge Data Output MCP Server connects your enterprise Gemini models directly to active MLS feeds. You pull specific property details using `get_property` or search whole regions with `search_properties_by_city` without writing complex integration boilerplate. Because Gemini handles massive context windows, your agent can ingest hundreds of raw listing payloads from a single call. You can feed these raw JSON outputs directly into your cloud analytics pipeline for immediate processing.

Analyze entire brokerage networks in BigQuery

Large-scale real estate operations require tracking entire offices and agent performance. The agent uses the MCP Server to call `list_offices` and `list_members` to map out brokerage hierarchies across multiple regional data systems. You can write these agent-curated datasets straight to BigQuery tables. This lets you combine live MLS directories with your internal performance metrics using standard SQL queries.

Run massive property comparisons in a single prompt

Traditional systems struggle to compare hundreds of properties because of token limits. By using `list_properties` alongside `list_media`, your agent gathers complete profiles for an entire ZIP code. The Google ADK framework passes these deep profiles into Gemini's million-token context window. Your agent then performs complex market comparisons and highlights pricing anomalies across the entire dataset.

Setup guide

Set up Bridge Data Output 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 Bridge Data Output 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="Bridge Data Output_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Bridge Data Output 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 Bridge Data Output. 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 Bridge Data Output MCP in Google ADK

You initialize McpToolset with your Vinkius HTTP endpoint and pass it to your LlmAgent. The framework registers tools like `list_recent_listings` so Gemini can call them natively during a conversation.

Yes, you pass a list of allowed tool names to the toolset configuration. This prevents the agent from running write operations or viewing sensitive directories like `list_members` if you only want to expose property searches.

Your agent queries active inventory using `search_properties_by_price`, then cross-references the results with historical sales data stored in BigQuery. This setup lets you build automated valuation models using real-time MLS inputs.

It does. The `list_media` tool retrieves photo and video metadata for listings, which your agent can process or send to Gemini Multimodal models for visual analysis.

All traffic between your Google Cloud environment and the Bridge API passes through an ephemeral V8 sandbox. Vinkius secures your credentials and listing data, ensuring no search parameters or agent rosters are cached on external servers.

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