Nearmap (High-Res Aerial Imagery & AI) MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Nearmap (High-Res Aerial Imagery & AI) as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Nearmap (High-Res Aerial Imagery & AI). "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Nearmap (High-Res Aerial Imagery & AI)?"
)
print(response)
asyncio.run(main())
* 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.
LlamaIndex agents combine Nearmap (High-Res Aerial Imagery & AI) tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Nearmap (High-Res Aerial Imagery & AI) MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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)
Why Use LlamaIndex with the Nearmap (High-Res Aerial Imagery & AI) MCP Server
LlamaIndex provides unique advantages when paired with Nearmap (High-Res Aerial Imagery & AI) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Nearmap (High-Res Aerial Imagery & AI) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Nearmap (High-Res Aerial Imagery & AI) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Nearmap (High-Res Aerial Imagery & AI), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Nearmap (High-Res Aerial Imagery & AI) tools were called, what data was returned, and how it influenced the final answer
Nearmap (High-Res Aerial Imagery & AI) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Nearmap (High-Res Aerial Imagery & AI) MCP Server delivers measurable value.
Hybrid search: combine Nearmap (High-Res Aerial Imagery & AI) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Nearmap (High-Res Aerial Imagery & AI) to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Nearmap (High-Res Aerial Imagery & AI) for fresh data
Analytical workflows: chain Nearmap (High-Res Aerial Imagery & AI) queries with LlamaIndex's data connectors to build multi-source analytical reports
Nearmap (High-Res Aerial Imagery & AI) MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Nearmap (High-Res Aerial Imagery & AI) to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Nearmap (High-Res Aerial Imagery & AI) to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpNearmap (High-Res Aerial Imagery & AI) + LlamaIndex FAQ
Common questions about integrating Nearmap (High-Res Aerial Imagery & AI) MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Nearmap (High-Res Aerial Imagery & AI) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
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Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Nearmap (High-Res Aerial Imagery & AI) to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.