Can the agent calculate walking paths across multiple floors?

Yes. When triggering `calculate_path` supplied with explicit coordinate pairings spanning different Level UUIDs, the Pointr engine bridges the wayfinding automatically. It factors in fixed transitions like elevators or stairs natively, feeding the Agent the comprehensive turn-by-turn array in JSON format seamlessly.

Is it possible to extract the giant raw map shapes for a given floor?

Absolutely. By initiating the query `get_level_map` tied securely to a single level UUID, the interface processes and downloads massive explicit geometries mapping out physical walls, traversable space nodes, and internal partitions explicitly generated by the Pointr pipeline.

Can I search for specific stores or bathrooms using text queries?

Yes. Pointr exposes dense fuzzy logic matching indexes. When the Agent executes `search_pois` feeding a literal keyword alongside the Target Building ID, it reliably unearths precisely mapped nodes conforming to 'Restroom', 'Exit', or custom store names embedded dynamically.

Pointr MCP Server for LlamaIndex 10 tools — connect in under 2 minutes

Name: Pointr
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Pointr as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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 Pointr. "
            "You have 10 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Pointr?"
    )
    print(response)

asyncio.run(main())

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* 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 Pointr MCP Server

Bring deep indoor location intelligence directly to your AI operations using the Pointr network. This MCP integration securely bridges your LLM to complex structural databases plotting multi-floor layouts, indoor geo-fencing, and Bluetooth Low Energy (BLE) beacon networks. Instead of navigating complicated dashboards to audit facility paths, simply instruct your local Agent to parse physical building parameters perfectly.

LlamaIndex agents combine Pointr tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through 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

Facility Exploration — Understand global deployments natively. Run list_buildings and list_levels to mathematically visualize vertical architectures and floor limits.
Precision Wayfinding — Query active Point of Interest objects. The agent leverages search_pois to find specific gates/stores, and dynamically invokes calculate_path predicting multi-floor walking paths avoiding structural walls.
Infrastructure Auditing — Ask the AI to evaluate BLE hardware mesh footprints using the list_beacons utility, verifying precisely where physical network sensors reside inside map geometries.
Geo-Fence Parsing — Interrogate proactive indoor trigger zones. list_geofences brings back complex logical polygons mapping where local alerts fire globally.

The Pointr 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 Pointr to LlamaIndex via MCP

Follow these steps to integrate the Pointr 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 Pointr

Why Use LlamaIndex with the Pointr MCP Server

LlamaIndex provides unique advantages when paired with Pointr through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Pointr tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Pointr tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Pointr, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Pointr tools were called, what data was returned, and how it influenced the final answer

Pointr + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Pointr MCP Server delivers measurable value.

Hybrid search: combine Pointr real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Pointr 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 Pointr for fresh data

Analytical workflows: chain Pointr queries with LlamaIndex's data connectors to build multi-source analytical reports

Pointr MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect Pointr to LlamaIndex via MCP:

calculate_path

Calculate the optimal indoor wayfinding path between two points

get_building

Retrieve detailed configuration for a specific Pointr building

get_level_map

Retrieve the floor plan map data for a specific building level

get_poi

Retrieve detailed information for a specific Pointr POI

list_beacons

List all BLE beacons deployed and registered in the Pointr platform

list_buildings

List all buildings registered in the Pointr indoor intelligence platform

list_geofences

List all indoor geofences configured in the Pointr platform

list_levels

List all floor levels for a specific Pointr building

list_pois

List all Points of Interest (POIs) registered in the Pointr platform

search_pois

Search for indoor Points of Interest by keyword

Example Prompts for Pointr in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Pointr immediately.

"List all active building deployments registered in our Pointr instance."

"Search for all restrooms securely listed under building ID `b1b2-c3c4`."

"Calculate indoor path from POI `poi_origin` to `poi_destination`."

Troubleshooting Pointr MCP Server with LlamaIndex

Common issues when connecting Pointr to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Pointr + LlamaIndex FAQ

Common questions about integrating Pointr MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Pointr tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.