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

Q: Can I query historical imagery dates using Nearmap (High-Res Aerial Imagery & AI) and Pydantic AI?

Yes. Your agent calls listsurveydates to get a structured list of capture times for any coordinate. This list is validated at runtime, ensuring your agent always works with real, formatted dates.

Q: What tools does Nearmap (High-Res Aerial Imagery & AI) provide for Pydantic AI to inspect property structures?

The agent can use getobliquetile to get multi-angle perspectives, getdsmelevationtile for height maps, and getaidetectedfeatures to extract vector shapes of buildings, pools, or solar panels.

Q: How do I connect this MCP Server to my Pydantic AI application?

You initialize the MCPToolset with the server's HTTP endpoint and pass it to your agent. The agent automatically discovers all ten tools, including getsurveymetadata and gettrueorthotile.

Fetch type-safe Nearmap aerial imagery and AI-detected property data with strict runtime validation in Pydantic AI.

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

Create your Vinkius account to connect Nearmap (High-Res Aerial Imagery & AI) to Pydantic AI 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 Pydantic AI

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Validate building footprints at runtime with Pydantic AI

Stop worrying about malformed geospatial payloads breaking your pipeline. When your agent calls `get_ai_detected_features` via this MCP connection, the response is validated against strict schemas before your code ever touches it. You get clean, predictable GeoJSON features every single time. The agent can inspect the available classification types by running `list_ai_feature_classes`. This ensures the agent only requests features that actually exist in Nearmap's schema, preventing validation errors before they happen.

Double-check imagery boundaries via this MCP Server

Avoid running expensive imagery requests on coordinates that have no data. Your agent can call `check_coverage_point` or `check_coverage_polygon` to confirm whether Nearmap has captured the area. The tool returns clear, validated boundaries that your application can trust. If coverage exists, the agent queries `list_survey_dates` to get a validated list of capture events. This lets you build reliable time-series analyses without dealing with unexpected null values or missing dates.

Retrieve structured elevation and tile data safely

Extract topographic heights with confidence using `get_dsm_elevation_tile`. Because the MCP Server maps responses directly to your schemas, your agent can calculate roof peaks and terrain slopes without silent data corruption. For visual verification, the agent pulls `get_vertical_tile` or `get_oblique_tile` to inspect the property. You get structured image metadata alongside the raw tile data, making it simple to feed the outputs directly into downstream vision models.

Setup guide

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

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "nearmap-high-res-aerial-imagery-ai-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Nearmap (High-Res Aerial Imagery & AI) tools.",
)

result = await agent.run("List recent Nearmap (High-Res Aerial Imagery & AI) transactions")
print(result.output)

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

The server returns structured JSON payloads that map directly to your Pydantic schemas. If a tool like `get_vertical_tile` or `get_ai_detected_features` returns unexpected data, the framework catches it instantly at runtime.

Yes. Your agent calls `list_survey_dates` to get a structured list of capture times for any coordinate. This list is validated at runtime, ensuring your agent always works with real, formatted dates.

The agent can use `get_oblique_tile` to get multi-angle perspectives, `get_dsm_elevation_tile` for height maps, and `get_ai_detected_features` to extract vector shapes of buildings, pools, or solar panels.

You initialize the `MCPToolset` with the server's HTTP endpoint and pass it to your agent. The agent automatically discovers all ten tools, including `get_survey_metadata` and `get_true_ortho_tile`.

Your latitude, longitude, and polygon coordinate data are processed inside secure, ephemeral V8 isolates. The server never writes your location queries or API credentials to persistent storage, keeping your proprietary property lists completely private.

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