Bring Indoor Positioning
to Pydantic AI

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.

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.

How it works

1. Append the Pointr connector directly into your MCP cluster
2. Introduce your explicit Enterprise Bearer Token into the configuration vault
3. Begin instructing your LLM to dissect and verify massive indoor mapping models natively

Who is this for?

• Facilities Managers — script AI routines to passively ping list_beacons and cross-reference them against active floor geometries to spot offline hardware.
• Spatial Data Engineers — command the assistant to extract massive layout configurations using get_level_map isolating routing constraints directly into code context.
• Aviation & Retail Planners — ask the LLM to run batch test queries calling calculate_path to simulate crowd flow and optimal step logic across massive airport or mall nodes.