How to Use the IndoorAtlas (Indoor Positioning) MCP in LlamaIndex

Q: Can I search through historical positioning sessions using LlamaIndex and IndoorAtlas (Indoor Positioning)?

Yes, by pulling raw traces with getsessiondata and indexing them. This allows you to run semantic queries over user paths, floor transitions, and signal accuracy metrics.

Q: How does the IndoorAtlas (Indoor Positioning) MCP Server help LlamaIndex with floor plan mapping?

The server provides the uploadfloorplangeojson tool, which your agent can use to upload layout files. Once uploaded, the agent indexes the geo-referenced coordinates to keep your spatial knowledge base updated.

Q: Can I run positioning queries directly from a Wi-Fi scan?

Yes, you can pass raw scan data to positionfromwifiscan to get instant coordinates. The agent can then index this estimated position to track real-time spatial trends.

Index live IndoorAtlas (Indoor Positioning) spatial data directly into LlamaIndex to query physical venues like a database.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect IndoorAtlas (Indoor Positioning) MCP to LlamaIndex

Create your Vinkius account to connect IndoorAtlas (Indoor Positioning) 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.

GDPR Free for Subscribers

Setup IndoorAtlas (Indoor Positioning) with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Turn Venue Metadata into Searchable LlamaIndex Nodes

The `get_venue_details` tool retrieves real-world dimensions and coordinates to turn physical layouts into searchable LlamaIndex nodes. The agent takes this data alongside `list_venues` to build a vector-based map index. Instead of writing custom database queries, you can ask your agent which buildings have incomplete maps. The engine searches the indexed venue metadata to find uncalibrated floors instantly.

Query Historical Sessions with Semantic Search

The `get_session_data` tool pulls raw coordinate traces so you can index past tracking data into your vector store. By retrieving session logs through `list_positioning_sessions`, you build a searchable history of visitor movement. You can ask the agent about foot traffic bottlenecks or common paths on the third floor. The agent queries the indexed coordinates to describe actual user behavior.

Build RAG Workflows for Calibration Quality

The `get_fingerprint_paths` tool returns walk paths so your agent can evaluate calibration coverage. It compares these routes against your uploaded floor plans to identify blind spots. When a user asks if a floor is ready for deployment, the engine checks the indexed radio map status from `list_floorplans`. It then tells you exactly which areas need more Wi-Fi fingerprints.

Setup guide

Set up IndoorAtlas (Indoor Positioning) 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 IndoorAtlas (Indoor Positioning) 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 IndoorAtlas (Indoor Positioning) tools.",
)
response = await agent.run("List recent IndoorAtlas (Indoor Positioning) data")

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by IndoorAtlas. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect IndoorAtlas (Indoor Positioning) now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about IndoorAtlas (Indoor Positioning) MCP in LlamaIndex

You connect the MCP tool spec to your agent and load the tool outputs into document objects. For example, you can query `get_venue_details` and index the building's geographic anchor points directly into your vector store.

Yes, by pulling raw traces with `get_session_data` and indexing them. This allows you to run semantic queries over user paths, floor transitions, and signal accuracy metrics.

The server provides the `upload_floorplan_geojson` tool, which your agent can use to upload layout files. Once uploaded, the agent indexes the geo-referenced coordinates to keep your spatial knowledge base updated.

Yes, you can pass raw scan data to `position_from_wifi_scan` to get instant coordinates. The agent can then index this estimated position to track real-time spatial trends.

All floor plans, Wi-Fi access point scans, and session coordinates are transmitted securely to the IndoorAtlas API. The Vinkius runtime executes the MCP server in a zero-trust sandbox, ensuring your spatial coordinates and MAC addresses are never cached.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript