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

Q: How do CrewAI agents collaborate using Nearmap (High-Res Aerial Imagery & AI)?

One agent uses checkcoveragepoint to verify flight boundaries, while a second agent calls getverticaltile to fetch the high-res imagery. They share this context in their joint memory space to analyze properties autonomously.

Q: Can a CrewAI team detect roof changes using Nearmap (High-Res Aerial Imagery & AI)?

Yes. An agent calls listsurveydates to get historical captures, then pulls tiles using getverticaltile across different timestamps. The team compares the visual changes to detect new construction or damage.

Q: How do I limit which Nearmap (High-Res Aerial Imagery & AI) MCP tools a CrewAI agent can use?

Use CrewAI's toolfilter configuration to expose only specific tools like getaidetectedfeatures to your analyst agent. This prevents other agents in the crew from making unauthorized spatial API calls.

Q: How does this MCP Server handle oblique angles for structural inspection?

Your inspector agent calls getobliquetile to fetch 3D-angled imagery from North, South, East, or West. This allows the crew to inspect building facades and walls that vertical views miss.

Deploy specialized agent teams using CrewAI to analyze Nearmap high-res aerial imagery and extract structural features.

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

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

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Coordinate Agent Teams with MCP Server

`check_coverage_polygon` coordinates spatial boundary verification across multiple specialized agents. In a CrewAI setup, your researcher agent runs this tool to map out valid flight boundaries before handing tasks over. Once confirmed, the analyst agent takes over to process the spatial data. This separation of duties prevents agents from wasting tokens on unmapped regions.

Multi-Agent Structural Inspection

`get_ai_detected_features` extracts building, pool, and solar panel vector geometries for specialized agent analysis. Your CrewAI analyst agent pulls these MCP-provided computer vision features while a separate auditor agent verifies them against tax records. By referencing `list_ai_feature_classes`, the team understands the exact taxonomy limits. They work in parallel to flag discrepancies between aerial data and public databases.

Topographic Analysis via CrewAI

`get_dsm_elevation_tile` extracts Digital Surface Model data to calculate topographic terrain and building peak heights. Your GIS agent uses this tool to evaluate slope and runoff risks. The agent then passes the terrain model to a risk-assessment agent, who pulls `get_vertical_tile` to inspect visual damage. This collaborative approach delivers highly accurate property risk reports automatically.

Setup guide

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

Prerequisites

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

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Nearmap (High-Res Aerial Imagery & AI) tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Nearmap (High-Res Aerial Imagery & AI) Analyst",
    goal="Access and analyze Nearmap (High-Res Aerial Imagery & AI) data via MCP.",
    backstory="Expert analyst with direct Nearmap (High-Res Aerial Imagery & AI) access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Nearmap (High-Res Aerial Imagery & AI) transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Nearmap (High-Res Aerial Imagery & AI) Analyst",
        goal="Access and analyze Nearmap (High-Res Aerial Imagery & AI) data via MCP.",
        backstory="Expert analyst with direct Nearmap (High-Res Aerial Imagery & AI) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Nearmap (High-Res Aerial Imagery & AI) transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

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

One agent uses `check_coverage_point` to verify flight boundaries, while a second agent calls `get_vertical_tile` to fetch the high-res imagery. They share this context in their joint memory space to analyze properties autonomously.

Yes. An agent calls `list_survey_dates` to get historical captures, then pulls tiles using `get_vertical_tile` across different timestamps. The team compares the visual changes to detect new construction or damage.

Use CrewAI's `tool_filter` configuration to expose only specific tools like `get_ai_detected_features` to your analyst agent. This prevents other agents in the crew from making unauthorized spatial API calls.

Your inspector agent calls `get_oblique_tile` to fetch 3D-angled imagery from North, South, East, or West. This allows the crew to inspect building facades and walls that vertical views miss.

Yes. Your coordinate queries and extracted vector geometries are processed inside Vinkius's zero-trust sandbox. CrewAI agents interact with the tools via secure SSE or stdio transports, ensuring your geospatial data never leaks to public LLM training sets.

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