Nearmap (High-Res Imagery) MCP. Audit site conditions and extract geometries from aerial views.
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Nearmap (High-Res Aerial Imagery & AI) connects your agent directly to world-class geospatial data. You can retrieve high-res aerial tiles, automatically extract vector geometries for buildings or solar panels, and audit survey coverage across specific polygons—all via conversation.
What your AI agents can do
Check coverage point
Verifies if Nearmap captured imagery over a specific point, tracking its history.
Check coverage polygon
Checks if the server has imagery data intersecting across a mapped area (a polygon).
Get ai detected features
Extracts vector shapes for buildings, pools, solar panels, and vegetation using AI computer vision.
Verify if Nearmap has captured imagery over specific points or complex mapped polygons.
Automatically detect and extract vector shapes for structures like buildings, solar panels, pools, and vegetation using AI computer vision.
Access angled imagery (oblique views) from four cardinal directions to inspect structural facades.
Generate elevation tiles that map out topographic terrain and building peak heights.
Check the earliest and latest survey dates available for a given location to track changes over time.
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Supported MCP Clients
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Nearmap (High-Res Imagery) MCP Server: 10 Geospatial Tools
Use these ten specialized functions to verify boundaries, extract features, and retrieve elevation data from aerial imagery for precise site analysis.
019d75dccheck coverage point
Verifies if Nearmap captured imagery over a specific point, tracking its history.
019d75dccheck coverage polygon
Checks if the server has imagery data intersecting across a mapped area (a polygon).
019d75dcget ai detected features
Extracts vector shapes for buildings, pools, solar panels, and vegetation using AI computer vision.
019d75dcget dsm elevation tile
Retrieves pixelated data mapping the terrain's surface model and building heights.
019d75dcget oblique tile
Gets angled 3D-view tiles pointing North, South, East, or West to locate structural details.
019d75dcget survey metadata
Queries specific flight parameters, including Ground Sample Distance (GSD) and optical capture details.
019d75dcget true ortho tile
Retrieves geometric top-down layers with zero parallax for perfect mapping alignment.
019d75dcget vertical tile
Gets high-resolution, straight-down (nadir) aerial imagery tiles covering the target area.
019d75dclist ai feature classes
Looks up all internal AI categories used for mapping features like roof arrays and buildings.
019d75dclist survey dates
Lists the chronological survey dates available that cover a specific target area.
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What you can do with this MCP connector
Your agent connects you directly to high-res geospatial data, letting you talk through massive amounts of aerial imagery and structural models. You don't need to know GIS; your AI client handles all the geometry.
Auditing Site Coverage and History
You can check if we captured imagery over any specific point using check_coverage_point, and it even tracks that history for you. To map out a whole area, run check_coverage_polygon to verify if data intersects across complex mapped boundaries. You'll never be stuck wondering when the best view was available; use list_survey_dates to pull every chronological survey date we’ve got for your target spot.
Extracting Structures with AI Vision
The server uses computer vision to extract vector shapes automatically. With get_ai_detected_features, you'll get the precise outlines for buildings, pools, solar panels, and vegetation. If you need to know what categories of features we can detect—like roof arrays or different building types—just run list_ai_feature_classes.
Getting Precise Views and Models
You get multiple ways to view the site, depending on what you're checking for. Use get_vertical_tile to grab straight-down (nadir) aerial imagery tiles, giving you high resolution over your whole area. For structural facades, run get_oblique_tile to access angled 3D views pointing North, South, East, or West.
When you need perfect mapping alignment with no parallax error, retrieve geometric top-down layers using get_true_ortho_tile. To understand the terrain's depth and building peaks, pull pixelated data for a Digital Surface Model (DSM) tile via get_dsm_elevation_tile.
Gathering Technical Data
If you need to know exactly how we flew or what quality the images are, you can query flight parameters. Use get_survey_metadata to get key details like Ground Sample Distance (GSD) and optical capture specs. You'll always have access to the raw data needed for accurate site assessment.
How Nearmap (High-Res Imagery) MCP Works
- 1 Subscribe to this server and provide your Nearmap API Key.
- 2 Tell your AI client what you need—for example, 'Get the latest vertical imagery for coordinates X.'
- 3 The agent runs the necessary tool (like
get_vertical_tile) and returns the specific tile URL or extracted feature data.
The bottom line is: it lets your AI client run complex geospatial queries that normally require specialized GIS software.
Who Is Nearmap (High-Res Imagery) MCP For?
Anyone who needs to assess physical property conditions from a distance. This isn't for general data workers; it’s for people whose job requires seeing what a building or piece of land looks like right now. Think insurance adjusters doing damage assessments, urban planners checking zoning boundaries, or solar installers mapping roof layouts.
Uses the server to audit property conditions and detect specific features (like roof wear) via conversation instead of making a manual site visit.
Verifies site dimensions, topographic elevations, and ideal building placements using high-res imagery tiles directly in their workflow.
Identifies existing solar arrays and extracts precise roof geometries to optimize installation planning and resource allocation.
What Changes When You Connect
- Verify coverage boundaries with
check_coverage_pointorcheck_coverage_polygon. You confirm the server has data for your exact location before you start planning a project, eliminating guesswork about available imagery. - Automate feature detection using
get_ai_detected_features. Instead of manually drawing bounding boxes, the AI pulls out high-confidence vector geometries for structures like solar arrays or pools instantly. - Get true geometric precision with
get_true_ortho_tileandget_vertical_tile. These tiles correct for lean and parallax errors, giving you perfect top-down maps needed for professional measurement. - Inspect facades without visiting the site. Use
get_oblique_tileto pull angled 3D views from North/South/East/West viewpoints, allowing structural auditing directly in your chat window. - Understand data quality with
get_survey_metadata. You can query specific flight parameters and GSD resolutions to know exactly how accurate the imagery is for your use case. - Track site changes over time using
list_survey_datesandlist_ai_feature_classes. You see a history of who captured the data and what features were identified across different dates.
Real-World Use Cases
Assessing Storm Damage
A claims adjuster needs to know if a roof was damaged in a specific area. They ask their agent for the most recent imagery and run get_ai_detected_features specifically targeting 'roof' or 'damage'. The server returns high-res tiles and outlines the affected areas, letting them generate an immediate report.
Zoning Compliance Check
An urban planner needs to verify if a proposed development fits within existing infrastructure. They use check_coverage_polygon to ensure the whole site is covered by imagery and then run get_dsm_elevation_tile to map the exact elevation peaks, confirming compliance before drawing plans.
Optimizing Solar Placement
A solar installer needs precise roof dimensions. They instruct their agent to 'Find all buildings and extract roof geometries.' The tool runs get_ai_detected_features, which returns ready-to-use vector data for every building, saving hours of manual measurement.
Tracking Site Development
A construction manager needs to track if a neighboring site has built new wings. They use list_survey_dates to get the timeline and then request get_vertical_tile for the latest date, allowing them to pinpoint any changes that occurred since their last inspection.
The Tradeoffs
Treating it like a general image search
Just asking 'Show me pictures of this building.' The agent will fail because the tool requires specific coordinates, dates, or feature types to work.
→
You must be specific. Start by checking coverage using check_coverage_point for the exact coordinate, then request the data type you need: e.g., 'Get me the true ortho tile and run get_ai_detected_features on it.'
Relying only on one view
Only asking for a vertical (straight down) image, which might miss structural details like facade damage or roof pitch.
→
Combine tools. Request the get_vertical_tile first, then follow up with 'Now, get me the oblique tile pointing North' to cover all angles.
Ignoring data quality
Accepting any imagery returned without knowing if it's old or low resolution. This leads to inaccurate reports.
→
Always check get_survey_metadata first. Use this tool to query the Ground Sample Distance (GSD) and flight date before you trust any tile retrieval function.
When It Fits, When It Doesn't
Use this server if your workflow requires mapping, elevation data, or automated feature extraction from aerial imagery. This is for geospatial engineers, civil planners, and surveyors.
Don't use it if you only need general text summarization or simple data lookups (like querying a contact list). If your goal is just 'What are the major trends in housing prices?', this won't help; you need a financial API. If your goal is to measure distances or map structures, though, this is exactly what you want.
When comparing coverage tools: Use check_coverage_point for single addresses and use check_coverage_polygon when you need to validate an entire parcel of land.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Nearmap. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manual site audits are slow. You spend hours clicking between Google Earth, GIS software, and photo archives just to get a basic view of the property's condition.
Today, assessing a large parcel requires jumping through hoops: pulling up coordinates in one tab, verifying coverage dates on another, downloading various tile types (vertical, oblique), and then manually running feature detection for every building or pool. You spend half your day just managing the data sources.
With this MCP server, you talk to your agent once. 'Show me the structure at 34.0522,-118.2437.' The agent runs `get_vertical_tile`, pulls the metadata via `get_survey_metadata`, and can even run `get_ai_detected_features` for you—all in a single chat response.
Get high-res aerial data using Nearmap (High-Res Imagery) MCP Server.
You no longer need to download ten different files from three different services just to get a full picture. You can ask the agent to check coverage across a polygon (`check_coverage_polygon`) and then immediately request an elevated view via `get_dsm_elevation_tile`—the system handles the complex chaining of data.
This capability means your analysis moves from 'What *might* be there?' to 'Here is exactly what is mapped.' It’s immediate, auditable, and accurate.
Common Questions About Nearmap (High-Res Imagery) MCP
How do I check if a polygon area has imagery coverage using check_coverage_polygon? +
You pass the coordinates of the complex shape to check_coverage_polygon. The server verifies if Nearmap imagery intersects across that entire mapped boundary, confirming you have data for every part of the required area.
What is the difference between get_vertical_tile and get_true_ortho_tile? +
The get_vertical_tile gives a straight-down view, but the get_true_ortho_tile corrects for geometric lean. The true ortho tile offers perfect, zero parallax alignment needed for precise measurement.
Can get_ai_detected_features identify solar panels? +
Yes, it can. This tool uses automated CV features bounds to extract vector geometries specifically for items like solar panels, alongside buildings and pools.
How do I track changes over time using list_survey_dates? +
Use list_survey_dates with the target coordinates. This tool iterates through available dates, giving you a chronological list of all capture times that cover your specific site.
Do I need to run get_oblique_tile for every direction? +
Yes, if you want a full audit. You must call get_oblique_tile separately and specify North, South, East, or West to ensure all structural facades are captured.
When I use `get_survey_metadata`, what specific flight parameters can I query? +
It returns explicit technical details about the aerial capture, including Ground Sample Distance (GSD) and optical hardware specs. You'll get a structured readout of the exact camera settings used for that specific flyover.
What does `list_ai_feature_classes` show me? +
This tool lists all available internal AI categories, not the data itself. It helps you understand what types of computer vision mappings are possible—like roof arrays or specific building types—before running extraction.
If I run `check_coverage_point`, how does it handle temporal history? +
It resolves a chronological array of available survey dates for that exact point. This means you can see every time Nearmap captured imagery crossing your specified coordinates.
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.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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