How to Use the CBRE Econometric Advisors (EA) MCP in Google ADK

Q: How do I add CBRE Econometric Advisors (EA) to Google ADK?

Install google-adk via pip. Initialize an McpToolset using StreamableHttpServerParameters with your Vinkius endpoint URL. Pass this toolset into your LlmAgent configuration.

Q: Does the MCP Server support specific asset sectors?

You can check the available categories using the listassetsectors tool. This ensures your agent only requests data for valid real estate types like industrial, retail, or multifamily.

Q: How do I restrict which CBRE Econometric Advisors (EA) tools my Google ADK agent can use?

Use the toolnames filter when setting up the toolset. This lets you expose only specific endpoints, like limiting the agent to read-only macroeconomic data.

Feed global real estate metrics into Google ADK to analyze CBRE Econometric Advisors (EA) data with Gemini.

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Connect CBRE Econometric Advisors (EA) MCP to Google ADK

Create your Vinkius account to connect CBRE Econometric Advisors (EA) 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 CBRE Econometric Advisors (EA) with Google ADK

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Query market metrics via MCP Server

`get_market_sector_data` fetches current performance numbers for a specific real estate sector in a defined market. Gemini models can ingest these metrics across hundreds of submarkets simultaneously thanks to their massive context windows. You ask for a national retail summary, and the agent pulls the exact rents and vacancy rates required to build it. Combining this with Google Cloud infrastructure changes how you process property data. You can pipe the output of `get_market_forecasts` directly into BigQuery for long-term storage and analysis. The framework handles the HTTP transport while your agent focuses on the economic reasoning.

Analyze macroeconomic drivers

`get_macroeconomic_data` extracts the core economic indicators that drive real estate demand. Population shifts, employment growth, and income changes become available to your Gemini agent as structured data. The agent uses this information to justify why a specific market might outperform its historical averages. Connecting these economic factors to property returns requires `get_market_cap_rates`. Your system pulls the NOI metrics and compares them against the employment data. Vertex AI can then run custom models on this combined dataset to spot emerging investment opportunities using the MCP integration.

Search the global property database

`list_cbre_markets` returns the geographical coverage available, spanning the US, Canada, Europe, and APAC regions. If your agent needs to drill down, `list_submarkets` reveals the localized neighborhoods within those major metros. This hierarchy ensures your Gemini queries always target valid geographical boundaries. Finding exact indices requires `search_cbre_assets`. The `McpToolset` class in Google ADK exposes this search function instantly. You initialize the toolset with your Vinkius URL, pass it to the `LlmAgent`, and your enterprise application starts querying global real estate assets.

Setup guide

Set up CBRE Econometric Advisors (EA) 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 CBRE Econometric Advisors (EA) 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="CBRE Econometric Advisors (EA)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to CBRE Econometric Advisors (EA) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about CBRE Econometric Advisors (EA) MCP in Google ADK

Install `google-adk` via pip. Initialize an `McpToolset` using `StreamableHttpServerParameters` with your Vinkius endpoint URL. Pass this toolset into your `LlmAgent` configuration.

The 1M+ token context window allows Gemini to process massive amounts of property data. Your agent can loop through dozens of markets and synthesize the results into a single report.

You can check the available categories using the `list_asset_sectors` tool. This ensures your agent only requests data for valid real estate types like industrial, retail, or multifamily.

Use the `tool_names` filter when setting up the toolset. This lets you expose only specific endpoints, like limiting the agent to read-only macroeconomic data.

Every request for NOI metrics or market forecasts executes within an ephemeral sandbox. Vinkius isolates your session completely. Your real estate search parameters and economic data requests vanish the second the connection closes.

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