How to Use the Nearmap (High-Res Aerial Imagery & AI) MCP in OpenAI Agents SDK

Q: Can I use Nearmap (High-Res Aerial Imagery & AI) with OpenAI Agents SDK to check regional imagery coverage?

Yes. Your agent can run checkcoveragepoint to verify if Nearmap has captured a specific location. It resolves the temporal history of the coordinate so you know exactly what imagery is available before pulling tiles.

Q: Can an agent automatically find roof characteristics using Nearmap (High-Res Aerial Imagery & AI) and OpenAI Agents SDK?

Yes, easily. The agent calls listaifeatureclasses to see what attributes are available, then runs getaidetectedfeatures on the target location. This returns structural boundaries, solar panels, and pool footprints directly to your agent.

Fetch real-time Nearmap aerial imagery and AI-detected property features directly within your OpenAI Agents SDK production pipelines.

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Connect Nearmap (High-Res Aerial Imagery & AI) MCP to OpenAI Agents SDK

Create your Vinkius account to connect Nearmap (High-Res Aerial Imagery & AI) to OpenAI Agents SDK 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 Nearmap (High-Res Aerial Imagery & AI) with OpenAI Agents SDK

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Run automated property audits with OpenAI Agents SDK

Let your agent check if a property falls within Nearmap's capture zones before triggering expensive image downloads. Connecting this MCP Server lets your agent call `check_coverage_point` or `check_coverage_polygon` to verify data availability over specific coordinates or parcels. This prevents wasted API credits on empty regions. Once verified, the agent pulls chronological survey records using `list_survey_dates` to understand how often a location gets updated. You get immediate answers about historical frequency without writing manual coordinate matching logic.

Extract structural features using this MCP Server

Your agent can pull precise vector geometry for buildings, pools, and solar setups without manual GIS work. By executing `get_ai_detected_features`, the agent parses raw computer vision outputs into structured data. You get exact footprints returned directly into your agent's context. To understand the types of attributes available, the agent queries `list_ai_feature_classes` first. This lets the agent dynamically adapt its questions based on what Nearmap actually tracked at that specific site, like roof material or vegetation overhang.

Pull high-res tiles and elevation profiles on demand

Get sub-decimeter top-down views using `get_true_ortho_tile` or `get_vertical_tile` to inspect roof conditions. If your agent needs to analyze roof pitch or building heights, it calls `get_dsm_elevation_tile` to extract topographic elevation data. For side-angle inspections, the agent pulls `get_oblique_tile` to look at structures from North, South, East, or West perspectives. The agent coordinates these calls autonomously based on the specific claims or underwriting task you assign it.

Setup guide

Set up Nearmap (High-Res Aerial Imagery & AI) MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Nearmap (High-Res Aerial Imagery & AI) tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Nearmap (High-Res Aerial Imagery & AI) tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Nearmap (High-Res Aerial Imagery & AI) tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Nearmap (High-Res Aerial Imagery & AI) Agent",
            instructions="You have access to Nearmap (High-Res Aerial Imagery & AI) tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Nearmap (High-Res Aerial Imagery & AI) MCP in OpenAI Agents SDK

Yes. Your agent can run `check_coverage_point` to verify if Nearmap has captured a specific location. It resolves the temporal history of the coordinate so you know exactly what imagery is available before pulling tiles.

The server exposes tools that take latitude and longitude points or polygon arrays. Your agent passes these parameters directly to tools like `get_vertical_tile` or `get_ai_detected_features` to get back raw imagery or structured GeoJSON data.

Yes, easily. The agent calls `list_ai_feature_classes` to see what attributes are available, then runs `get_ai_detected_features` on the target location. This returns structural boundaries, solar panels, and pool footprints directly to your agent.

No. The server automatically registers its tools with your agent when you initialize the server connection. The agent discovers tools like `get_survey_metadata` and uses them dynamically based on user prompts.

The server only processes the geospatial coordinates and bounding boxes you pass to query imagery. All traffic runs through a zero-trust V8 sandbox, meaning your location queries and API tokens are never logged or stored on external servers.

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