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 OpenAI Agents SDK 10 tools — connect in under 2 minutes

Name: Pointr
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 10 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Pointr through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.

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 agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="Pointr Assistant",
            instructions=(
                "You help users interact with Pointr. "
                "You have access to 10 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Pointr"
        )
        print(result.final_output)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

The OpenAI Agents SDK auto-discovers all 10 tools from Pointr through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Pointr, another analyzes results, and a third generates reports, all orchestrated through Vinkius.

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 OpenAI Agents SDK 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 OpenAI Agents SDK via MCP

Follow these steps to integrate the Pointr MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 10 tools from Pointr

Why Use OpenAI Agents SDK with the Pointr MCP Server

OpenAI Agents SDK provides unique advantages when paired with Pointr through the Model Context Protocol.

Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

Pointr + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Pointr MCP Server delivers measurable value.

Automated workflows: build agents that query Pointr, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents. one queries Pointr, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Pointr tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Pointr to resolve tickets, look up records, and update statuses without human intervention

Pointr MCP Tools for OpenAI Agents SDK (10)

These 10 tools become available when you connect Pointr to OpenAI Agents SDK 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 OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK 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 OpenAI Agents SDK

Common issues when connecting Pointr to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Pointr + OpenAI Agents SDK FAQ

Common questions about integrating Pointr MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.