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

Q: How does this MCP Server verify spatial boundaries in Mastra AI?

The agent executes checkcoveragepolygon to cross-reference your target polygon against mapped flight boundaries. This ensures the workflow only requests tiles for regions that actually have active survey coverage.

Q: How do I handle missing imagery dates during an automated run?

Your agent uses listsurveydates to pull all valid flight dates first. If the desired date is missing, the Mastra AI workflow branches to the nearest available capture date dynamically.

Build automated GIS workflows with Mastra AI and Nearmap to trigger structural checks and elevation audits.

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

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

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Automate Property Audits with Mastra AI

`get_ai_detected_features` extracts structural vector data like building footprints and solar arrays for automated property assessments. Mastra AI agents use these MCP-extracted computer vision outputs to trigger workflow branches when specific structures are found. If a pool or solar panel is detected, the workflow automatically branches to evaluate property risk. This lets you run complex spatial evaluations without manual intervention.

Multi-Angle Analysis via MCP Server

`get_oblique_tile` retrieves 3D-angled imagery pointing North, South, East, or West to inspect structural facades. Your Mastra AI agent calls this tool when top-down vertical imagery is blocked by shadows or roof overhangs. By pairing this with `get_survey_metadata`, the agent verifies the exact optical flight details and ground sample distance (GSD). You get absolute certainty on the resolution before running automated visual checks.

Sequential Elevation Mapping

`get_dsm_elevation_tile` extracts Digital Surface Model (DSM) pixels to map topographic terrain and building peak heights. Mastra AI manages this step sequentially, passing the height maps to downstream analysis steps. If the elevation data indicates a steep slope, the agent automatically triggers `check_coverage_polygon` to verify boundary accuracy. This prevents pipeline crashes on edge-case terrain.

Setup guide

Set up Nearmap (High-Res Aerial Imagery & AI) MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Nearmap (High-Res Aerial Imagery & AI) tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "nearmap-high-res-aerial-imagery-ai-mcp-client",
  servers: {
    "nearmap-high-res-aerial-imagery-ai-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Nearmap (High-Res Aerial Imagery & AI) Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Nearmap (High-Res Aerial Imagery & AI) tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Nearmap (High-Res Aerial Imagery & AI) transactions"
);
console.log(result.text);

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

Mastra AI uses its built-in workflow engine to handle retries with exponential backoff when calling `get_vertical_tile`. If you hit API limits during a batch run, the agent pauses and retries without dropping the execution state.

Yes. You can configure Mastra AI to pause the workflow before calling `get_ai_detected_features`. An operator reviews the target coordinates, approves the step, and the agent continues extracting the building footprints.

The agent executes `check_coverage_polygon` to cross-reference your target polygon against mapped flight boundaries. This ensures the workflow only requests tiles for regions that actually have active survey coverage.

Your agent uses `list_survey_dates` to pull all valid flight dates first. If the desired date is missing, the Mastra AI workflow branches to the nearest available capture date dynamically.

All spatial queries, coordinate inputs, and tile images pass through Vinkius's ephemeral sandbox. Mastra AI processes the imagery in memory, and no persistent storage of your location data occurs outside your active workflow session.

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