How high is the resolution of Nearmap imagery compared to standard satellite maps?

Nearmap provides high-resolution aerial imagery at sub-15cm (approx. 6 inch) Ground Sample Distance (GSD). This is significantly sharper than typical satellite imagery (usually 30cm-50cm), allowing your agent to identify fine site details like roof vents, pavement cracks, or pool conditions.

Can my agent automatically detect solar panels or swimming pools on a property?

Yes. Use the `get_ai_detected_features` tool. Nearmap's AI analyzes recent surveys to return vector geometries for specific feature classes like 'Solar Panels' or 'Swimming Pools', including confidence thresholds and area measurements.

What is 'Oblique' imagery and how do I access it through the agent?

Oblique imagery is captured at a 45-degree angle rather than straight down. Use the `get_oblique_tile` tool and specify a direction (North, South, East, West). This allows your agent to audit building facades, structural heights, and side-view property details.

Nearmap (High-Res Aerial Imagery & AI) MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Nearmap (High-Res Aerial Imagery & AI)
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Nearmap (High-Res Aerial Imagery & AI) through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Nearmap (High-Res Aerial Imagery & AI) "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Nearmap (High-Res Aerial Imagery & AI)?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Nearmap (High-Res Aerial Imagery & AI) MCP Server

Connect your Nearmap account to any AI agent and take full control of world-class high-resolution aerial imagery, geospatial AI insights, and topographic surface models through natural conversation.

Pydantic AI validates every Nearmap (High-Res Aerial Imagery & AI) tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Aerial Orchestration — Retrieve sub-15cm resolution vertical (nadir) imagery tiles using Web Mercator coordinates to verify site conditions directly from your agent
AI Feature Extraction — Automatically detect and extract vector geometries for buildings, swimming pools, solar panels, and vegetation within specific geographic radii
Perspective Oblique Imagery — Access 3D-angled imagery from North, South, East, and West viewpoints to audit structural facades and building heights securely
Coverage & Survey Audit — Verify imagery availability across specific points or complex polygons and retrieve chronological survey dates to track site changes over time
Topographic Modeling — Extract Digital Surface Model (DSM) elevation tiles to analyze terrain peaks, building heights, and surface volumes natively within your workspace
True Ortho Visualization — Retrieve geometric lean-corrected top-down layers providing zero parallax alignments for perfect geospatial mapping and precision measurement
Survey Metadata — Query explicit flight parameters including Ground Sample Distance (GSD) and optical capture details for any specific aerial flyover

The Nearmap (High-Res Aerial Imagery & AI) MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Nearmap (High-Res Aerial Imagery & AI) to Pydantic AI via MCP

Follow these steps to integrate the Nearmap (High-Res Aerial Imagery & AI) MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Nearmap (High-Res Aerial Imagery & AI) with type-safe schemas

Why Use Pydantic AI with the Nearmap (High-Res Aerial Imagery & AI) MCP Server

Pydantic AI provides unique advantages when paired with Nearmap (High-Res Aerial Imagery & AI) through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Nearmap (High-Res Aerial Imagery & AI) integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Nearmap (High-Res Aerial Imagery & AI) connection logic from agent behavior for testable, maintainable code

Nearmap (High-Res Aerial Imagery & AI) + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Nearmap (High-Res Aerial Imagery & AI) MCP Server delivers measurable value.

Type-safe data pipelines: query Nearmap (High-Res Aerial Imagery & AI) with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Nearmap (High-Res Aerial Imagery & AI) tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Nearmap (High-Res Aerial Imagery & AI) and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Nearmap (High-Res Aerial Imagery & AI) responses and write comprehensive agent tests

Nearmap (High-Res Aerial Imagery & AI) MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Nearmap (High-Res Aerial Imagery & AI) to Pydantic AI via MCP:

check_coverage_point

Verify Nearmap capture boundaries matching geospatial point resolving temporal history arrays

check_coverage_polygon

Verify internal aerial survey boundary masks checking imagery intersections across mapped polygons

get_ai_detected_features

Extract vector geometry parsing buildings, pools, vegetation, solar panels via automated CV features bounds

get_dsm_elevation_tile

Extract pixelated Digital Surface Model mapping topographic terrain and building peak heights

get_oblique_tile

Retrieve oblique perspective 3D-angled imagery tiles pointing North, South, East, West locating structural targets

get_survey_metadata

Query explicitly bounded survey parameters finding GSD resolutions finding optical flight details

get_true_ortho_tile

Retrieve lean-corrected true geometric top-down bounding layers capturing zero parallax alignments

get_vertical_tile

Retrieve high-resolution vertical static nadir aerial imagery tiles tracking captured boundaries

list_ai_feature_classes

Lookup all internal AI category taxonomies evaluating computer vision mappings detecting roof arrays

list_survey_dates

Iterate chronological availability boundaries mapping all temporal captures crossing target nodes

Example Prompts for Nearmap (High-Res Aerial Imagery & AI) in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Nearmap (High-Res Aerial Imagery & AI) immediately.

"Get the latest high-res vertical imagery for coordinates '34.0522,-118.2437'"

"Detect all solar panels within a 100m radius of this point: [lat,lon]"

"Show me the oblique view from the North for this building coordinate"

Troubleshooting Nearmap (High-Res Aerial Imagery & AI) MCP Server with Pydantic AI

Common issues when connecting Nearmap (High-Res Aerial Imagery & AI) to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Nearmap (High-Res Aerial Imagery & AI) + Pydantic AI FAQ

Common questions about integrating Nearmap (High-Res Aerial Imagery & AI) MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your Nearmap (High-Res Aerial Imagery & AI) MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.