How to Use the Nearmap (High-Res Aerial Imagery & AI) MCP in Google ADK

Q: Can Gemini verify survey dates using Nearmap (High-Res Aerial Imagery & AI) in Google ADK?

Yes. The agent uses listsurveydates to find every historical capture for a specific location. This allows your agent to perform change detection over time by comparing older tiles with recent ones.

Q: What is the easiest way to extract building footprints using Nearmap (High-Res Aerial Imagery & AI) and Google ADK?

You configure the agent with the server, and it calls getaidetectedfeatures for the target coordinates. The tool returns precise GeoJSON footprints of buildings, which your agent can immediately parse or store.

Q: Does this MCP Server support both standard and orthorectified imagery?

Yes. The server includes getverticaltile for standard nadir views and gettrueorthotile for lean-corrected, parallax-free imagery. Your agent decides which tool fits the analysis requirements.

Feed sub-decimeter Nearmap aerial imagery and AI-parsed property data directly into your Google ADK enterprise agents.

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Connect Nearmap (High-Res Aerial Imagery & AI) MCP to Google ADK

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Setup Nearmap (High-Res Aerial Imagery & AI) with Google ADK

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Pull top-down and angled imagery using Google ADK

Let Gemini models inspect physical properties using actual aerial views instead of outdated satellite maps. This MCP integration lets your agent call `get_vertical_tile` or `get_true_ortho_tile` to retrieve clear, top-down imagery of any address. Having these files handy gives your agent the exact visual proof needed for underwriting or site planning. When flat views aren't enough, the agent pulls `get_oblique_tile` to examine the sides of buildings from four cardinal directions. This oblique perspective helps your agent identify structural features, facade damage, or equipment placements.

Analyze terrain heights via this MCP Server

Your agent can evaluate slope and roof heights by fetching digital surface models directly. By invoking `get_dsm_elevation_tile`, the agent gets gridded elevation data showing the heights of buildings and surrounding terrain. Before pulling elevation tiles, the agent checks `get_survey_metadata` to confirm the ground sample distance and flight details. This ensures your analysis relies on the highest resolution data available for that specific date.

Export AI-detected building footprints to BigQuery

Combine aerial intelligence with your enterprise database by letting your agent query `get_ai_detected_features`. The agent extracts vector geometry for structures, pools, and vegetation over your target area. You can then pipe this structured data directly into BigQuery. To keep queries efficient, the agent uses `check_coverage_polygon` to verify boundaries before running heavy extractions. This saves processing time and ensures your pipeline only targets areas with active Nearmap coverage.

Setup guide

Set up Nearmap (High-Res Aerial Imagery & AI) 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 Nearmap (High-Res Aerial Imagery & AI) 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="Nearmap (High-Res Aerial Imagery & AI)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Nearmap (High-Res Aerial Imagery & AI) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Nearmap (High-Res Aerial Imagery & AI) MCP in Google ADK

Your agent connects to the MCP Server and gains instant access to tools like `get_vertical_tile` and `get_ai_detected_features`. It can query these tools to retrieve imagery and vector data, then feed the results directly into Gemini's long-context window.

Yes. The agent uses `list_survey_dates` to find every historical capture for a specific location. This allows your agent to perform change detection over time by comparing older tiles with recent ones.

You configure the agent with the server, and it calls `get_ai_detected_features` for the target coordinates. The tool returns precise GeoJSON footprints of buildings, which your agent can immediately parse or store.

Yes. The server includes `get_vertical_tile` for standard nadir views and `get_true_ortho_tile` for lean-corrected, parallax-free imagery. Your agent decides which tool fits the analysis requirements.

All geospatial coordinates and polygon boundaries passed to the server are processed within isolated, ephemeral containers. Your sensitive location data and API keys are never exposed to third parties or used for model training.

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