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

Q: How does the IndoorAtlas (Indoor Positioning) MCP Server help AutoGen manage concurrent map builds?

Your coordinator agent can manage the queue by checking listfloorplans before triggering triggermapgeneration. This prevents multiple agents from starting conflicting map generation tasks at the same time.

Q: Can agents upload floor plans autonomously?

Yes, an agent can take a GeoJSON file and use uploadfloorplangeojson to register the floor plan. A second QA agent then verifies the geo-alignment metadata before proceeding.

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Setup IndoorAtlas (Indoor Positioning) with AutoGen

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Multi-Agent Calibration Audits with AutoGen Agents

The `get_fingerprint_paths` tool provides the raw walk routes that your AutoGen agents can analyze during a calibration quality debate. While one agent pulls these paths, a coordinator agent checks the venue setup with `get_venue_details`. They compare notes to decide if the floor needs more work. If they agree the coverage is too sparse, they automatically generate a report pinpointing the exact coordinates that need more Wi-Fi scans.

Coordinated Radio Map Generation

The `upload_floorplan_geojson` tool lets one agent register a layout while another agent monitors the build status. They coordinate by calling `list_floorplans` to track progress. If the build takes too long, the monitoring agent can alert the coordinator to pause other tasks. They negotiate when to trigger `trigger_map_generation` to avoid overloading your API limits.

Collaborative Session Analysis

The `list_positioning_sessions` tool lets diagnostic agents retrieve live sessions to troubleshoot drops in positioning accuracy. They pull the raw coordinate traces via `get_session_data` to locate the failure point. Meanwhile, a network agent runs test scans using `position_from_wifi_scan` to check signal strength. They debate whether the issue is caused by a broken router or bad calibration paths.

Setup guide

Set up IndoorAtlas (Indoor Positioning) MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes IndoorAtlas (Indoor Positioning) tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="IndoorAtlas (Indoor Positioning)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent IndoorAtlas (Indoor Positioning) data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="IndoorAtlas (Indoor Positioning)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent IndoorAtlas (Indoor Positioning) data")
    print(result.messages[-1].content)

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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.

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Common questions about IndoorAtlas (Indoor Positioning) MCP in AutoGen

You register the MCP server's tools with your assistant agents using the adapter. One agent can call `list_venues` to find a building, while another agent uses that ID to inspect the floor plans.

Yes, you can set up a conversation where one agent analyzes coordinate drift from `get_session_data` and another checks the calibration paths. They collaborate to find where the positioning model is failing.

Your coordinator agent can manage the queue by checking `list_floorplans` before triggering `trigger_map_generation`. This prevents multiple agents from starting conflicting map generation tasks at the same time.

Yes, an agent can take a GeoJSON file and use `upload_floorplan_geojson` to register the floor plan. A second QA agent then verifies the geo-alignment metadata before proceeding.

Your venue configurations, floor coordinates, and Wi-Fi signal strengths are processed entirely in memory within a secure V8 sandbox. No positioning traces or floor plan files are ever saved on Vinkius MCP servers.

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