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

Q: How do I configure IndoorAtlas (Indoor Positioning) for a CrewAI team?

Pass the server URL in the mcps parameter of your Agent definition. This allows your crew to use tools like getvenuedetails and listfloorplans immediately.

Deploy autonomous location monitoring crews using CrewAI and the IndoorAtlas (Indoor Positioning) MCP Server.

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Connect IndoorAtlas (Indoor Positioning) MCP to CrewAI

Create your Vinkius account to connect IndoorAtlas (Indoor Positioning) to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup IndoorAtlas (Indoor Positioning) with CrewAI

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CrewAI autonomous site monitoring

Assign a monitor agent to poll `list_positioning_sessions` for new activity. When a session appears, the agent can decide whether to fetch deep analytics or ignore the noise. This creates a self-regulating system that keeps an eye on your facility health. Your agents act as the eyes on the ground, processing location streams without you needing to supervise every minute.

Automated calibration and quality checks

Task your research agent with checking `get_venue_details` to verify if your mapping completeness metrics are up to standard. If they dip, the agent triggers a maintenance response. By comparing current coverage to your requirements, the crew identifies exactly which floorplans need attention. This prevents positioning degradation before users notice a drop in accuracy.

Hierarchical IndoorAtlas (Indoor Positioning) ops

Structure your crew so a moderator agent validates data from `position_from_wifi_scan` before taking action. This ensures that only high-confidence location fixes reach your downstream systems. If the uncertainty radius is too large, the agent can escalate the issue or request more calibration. It's a closed-loop system where agents manage the accuracy of your positioning data autonomously.

Setup guide

Set up IndoorAtlas (Indoor Positioning) MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke IndoorAtlas (Indoor Positioning) tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="IndoorAtlas (Indoor Positioning) Analyst",
    goal="Access and analyze IndoorAtlas (Indoor Positioning) data via MCP.",
    backstory="Expert analyst with direct IndoorAtlas (Indoor Positioning) access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent IndoorAtlas (Indoor Positioning) transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

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

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="IndoorAtlas (Indoor Positioning) Analyst",
        goal="Access and analyze IndoorAtlas (Indoor Positioning) data via MCP.",
        backstory="Expert analyst with direct IndoorAtlas (Indoor Positioning) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent IndoorAtlas (Indoor Positioning) transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

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.

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Common questions about IndoorAtlas (Indoor Positioning) MCP in CrewAI

Pass the server URL in the `mcps` parameter of your Agent definition. This allows your crew to use tools like `get_venue_details` and `list_floorplans` immediately.

Yes, CrewAI agents share memory. One agent can fetch session data, and the next can analyze it to optimize paths, ensuring everyone works from the same set of coordinates.

You can restrict which tools each agent uses by applying a tool filter in the MCP client setup. This ensures your research agent doesn't accidentally trigger destructive actions like venue creation.

Enable the verbose mode in your crew configuration. You will see the agent's thought process and the exact output from tools like `list_positioning_sessions` in the terminal.

We enforce strict zero-trust access by requiring a unique endpoint token for every connection. Your location traces and floorplan data never leave the secure, ephemeral sandbox environment during agent execution.

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