How to Use the Green Street MCP in Google ADK

Run commercial real estate analytics inside Google ADK to ground your enterprise agents with institutional REIT data.

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Connect Green Street MCP to Google ADK

Create your Vinkius account to connect Green Street 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 Green Street with Google ADK

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Ground Enterprise Agents in REIT Financials

The `list_companies` tool provides a clean index of all covered REITs and real estate companies. Your Google ADK agent queries this list to initialize targeted financial audits without manual spreadsheet lookups. Combine this with `get_company_summary` to pull fundamental balance sheet metrics. Gemini's massive context window lets you feed decades of these summaries into a single prompt to map long-term capital allocation trends.

Analyze Market Sectors with Google ADK

The `get_market_sector_summary` tool retrieves localized analytics and grades for specific property types. This MCP Server integration lets your BigQuery pipelines feed market performance metrics directly to your reasoning agents. You can also call `list_sectors` to map out the entire commercial real estate universe. Your agent uses this taxonomy to structure unstructured market news retrieved via `get_news_articles`.

Model Future Real Estate Scenarios

The `get_forecast_scenarios` tool retrieves forward-looking projections for key commercial real estate markets. Your Gemini-powered agent uses these scenarios to run stress tests against your existing portfolio assets. Run these projections alongside `get_market_projections` to analyze net operating income trends. This lets you build automated risk-assessment workflows that flag vulnerable assets before macro shifts impact your returns.

Setup guide

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

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Common questions about Green Street MCP in Google ADK

You pass the MCP server parameters into the McpToolset constructor. This automatically exposes the real estate endpoints to your Gemini models.

Yes, this toolset integrates directly with Vertex AI agents. You can combine Google ADK with these real estate tools to build enterprise-grade research pipelines.

The Vinkius infrastructure manages connection pooling and rate limits under the hood. Your Python application only needs to handle the standard tool execution calls.

Yes, your agent can use the news search tool to track industry developments. It pulls relevant articles to contextualize changes in market grades.

Your portfolio breakdowns and query parameters are processed in zero-trust, isolated runtimes. No commercial real estate data or API payloads are saved to persistent storage, protecting your firm's IP.

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