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

Q: What makes using Nearmap (High-Res Aerial Imagery & AI) with LlamaIndex different from just storing API results in a database?

LlamaIndex creates a semantic index. It understands the meaning of the data from tools like getaidetectedfeatures, allowing you to perform conceptual searches, not just exact SQL-style queries. It connects the raw data to your language model's understanding.

Q: How do I keep my LlamaIndex knowledge base updated with the latest data from Nearmap (High-Res Aerial Imagery & AI)?

You can create an agent that runs on a schedule. It uses listsurveydates to check for new imagery and, if found, automatically runs your indexing pipeline to fetch new tiles and AI features, keeping your knowledge base current.

Build RAG apps with LlamaIndex that index and query real-time Nearmap aerial imagery and AI-detected features.

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

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

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Index Property Features, Not Just Documents

Don't just fetch data—index it. Use LlamaIndex to run `get_ai_detected_features` across a whole portfolio of properties. The vector geometries for every building, pool, and solar panel are automatically parsed and ingested into a searchable knowledge base. Now your AI client can query a structured index of real-world objects. Ask it to "find all properties with pools and solar panels added after 2022" and it gets answers from the Nearmap data you've already indexed. It's search, not just repeated API calls.

Ground Queries in Verifiable Imagery

When your LlamaIndex agent answers a question, it cites its sources. It can retrieve the exact survey date with `list_survey_dates` and the specific image tile with `get_vertical_tile` that proves its answer. It's not just making things up. This creates a verifiable audit trail for every response. You get the answer, the GeoJSON from `get_ai_detected_features`, and the underlying imagery all linked together. It's RAG for the physical world, grounded in specific, time-stamped aerial photos.

Turn Your MCP Server into a Knowledge Engine

LlamaIndex turns this Nearmap MCP Server from a simple API wrapper into a continuously updated knowledge engine. You can build a system that periodically calls `list_survey_dates` to check for new flyovers in your areas of interest. When new imagery is available, your agent can automatically fetch it, run AI detection with `get_ai_detected_features`, and update your vector index. Your RAG application stays current with the real world without any manual work. It's the best of both worlds: live data access and fast, indexed retrieval.

Setup guide

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

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Nearmap (High-Res Aerial Imagery & AI) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Nearmap (High-Res Aerial Imagery & AI) tools.",
)
response = await agent.run("List recent Nearmap (High-Res Aerial Imagery & AI) data")

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

LlamaIndex wraps the Nearmap tools, so when you call a tool like `get_ai_detected_features`, it can take the resulting GeoJSON payload and ingest it directly into a configured vector store. This makes the attributes and geometries of detected objects searchable.

Yes. Once the AI features are indexed, you can ask natural language questions like "show me properties with a roof area over 2,000 sq ft" or "list addresses with new construction since the last survey." Your LlamaIndex agent queries the index to find the answers.

LlamaIndex creates a semantic index. It understands the *meaning* of the data from tools like `get_ai_detected_features`, allowing you to perform conceptual searches, not just exact SQL-style queries. It connects the raw data to your language model's understanding.

You can create an agent that runs on a schedule. It uses `list_survey_dates` to check for new imagery and, if found, automatically runs your indexing pipeline to fetch new tiles and AI features, keeping your knowledge base current.

Your LlamaIndex application sends geospatial coordinates to the MCP Server and receives image tiles and vector geometry in return. The connection is encrypted, and Vinkius securely manages the Nearmap API token, so it's never exposed in your application or stored in your index.

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