Can I determine a position using Wi-Fi scans through my agent?

Yes. The `position_from_wifi_scan` tool allows you to submit observed Wi-Fi signal strengths. Your agent will return estimated coordinates and floor level, which is perfect for server-side positioning logic.

How can I analyze visitor traffic patterns in our building?

Use the `list_positioning_sessions` tool to retrieve historical traces. Your agent can help you analyze occupancy patterns and dwell times by processing the timestamped coordinate fixes from past sessions.

IndoorAtlas (Indoor Positioning) MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: IndoorAtlas (Indoor Positioning)
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to IndoorAtlas (Indoor Positioning) through Vinkius, pass the Edge URL in the `mcps` parameter and every IndoorAtlas (Indoor Positioning) tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="IndoorAtlas (Indoor Positioning) Specialist",
    goal="Help users interact with IndoorAtlas (Indoor Positioning) effectively",
    backstory=(
        "You are an expert at leveraging IndoorAtlas (Indoor Positioning) tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in IndoorAtlas (Indoor Positioning) "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

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

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About IndoorAtlas (Indoor Positioning) MCP Server

Connect your IndoorAtlas account to any AI agent and take full control of your smart building infrastructure and indoor positioning services through natural conversation.

When paired with CrewAI, IndoorAtlas (Indoor Positioning) becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call IndoorAtlas (Indoor Positioning) tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Venue Management — List all registered indoor venues and retrieve detailed metadata including geographic anchor points and floor counts directly from your agent
Floorplan Orchestration — Upload new floor plans as GeoJSON and manage geo-referencing to real-world coordinates for accurate indoor positioning
Map Generation — Trigger the radio map generation process to compute positioning models from signal fingerprint data and floor geometry
Analytics & Sessions — Retrieve historical positioning sessions and trace data to analyze occupancy patterns, dwell times, and path optimization
Wi-Fi Positioning — Determine indoor location from Wi-Fi scans using the Positioning API to receive estimated coordinates and floor levels
Calibration Audit — Inspect fingerprinting walk paths to assess calibration coverage and identify areas needing additional signal mapping

The IndoorAtlas (Indoor Positioning) MCP Server exposes 10 tools through the Vinkius. Connect it to CrewAI 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 IndoorAtlas (Indoor Positioning) to CrewAI via MCP

Follow these steps to integrate the IndoorAtlas (Indoor Positioning) MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

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

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from IndoorAtlas (Indoor Positioning)

Why Use CrewAI with the IndoorAtlas (Indoor Positioning) MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with IndoorAtlas (Indoor Positioning) through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

IndoorAtlas (Indoor Positioning) + CrewAI Use Cases

Practical scenarios where CrewAI combined with the IndoorAtlas (Indoor Positioning) MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries IndoorAtlas (Indoor Positioning) for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries IndoorAtlas (Indoor Positioning), analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain IndoorAtlas (Indoor Positioning) tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries IndoorAtlas (Indoor Positioning) against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

IndoorAtlas (Indoor Positioning) MCP Tools for CrewAI (10)

These 10 tools become available when you connect IndoorAtlas (Indoor Positioning) to CrewAI via MCP:

create_venue

The venue serves as the top-level container for floor plans and positioning data. After creation, upload floor plan images and calibrate for positioning accuracy. Create a new indoor venue in the IndoorAtlas platform by specifying the building name, geographic coordinates of the entrance, and initial configuration parameters for indoor positioning deployment

get_fingerprint_paths

Returns GeoJSON LineString features representing calibration paths. Use to assess calibration coverage and identify areas of the floor that need additional fingerprinting for better positioning accuracy. Retrieve the fingerprinting walk paths recorded for a specific floor plan as GeoJSON, showing the routes surveyors walked while collecting Wi-Fi/BLE signal data for positioning calibration

get_session_data

Returns the complete position trace as a series of timestamped fixes. Use for path visualization, behavioral analysis, and positioning quality assessment. Large sessions may contain thousands of position fixes. Retrieve the full positioning trace data for a specific IndoorAtlas session, including timestamped coordinate fixes, floor transitions, accuracy metrics, and sensor readings throughout the session duration

get_venue_details

Returns the venue configuration including coordinate reference, building dimensions, and mapping completeness metrics. Use to inspect a venue before deploying positioning or wayfinding features. Retrieve detailed metadata for a specific IndoorAtlas venue including its geographic anchor point, floor count, total mapped area, calibration status, and associated floor plan identifiers

list_floorplans

Returns an array of floor plan metadata objects ordered by floor number. Each entry includes the plan dimensions, pixel-to-meter scale, and whether radio map generation has been completed. List all floor plans uploaded to a specific IndoorAtlas venue, returning floor plan IDs, floor numbers, dimensions, geo-alignment status, and map generation readiness for each level of the building

list_positioning_sessions

Returns a paginated list of positioning sessions. Each session represents a continuous period of indoor tracking by a single device. Use for occupancy analytics, dwell time analysis, and path optimization studies. List historical indoor positioning sessions recorded by IndoorAtlas, returning session IDs, start/end times, venue associations, and device information for analytics and path replay

list_venues

Returns an array of venue objects. Each venue represents a physical building that has been set up for indoor positioning. Use to discover available venues before requesting floor plans or positioning data. List all indoor venues registered in your IndoorAtlas organization, returning venue IDs, names, geographic coordinates, and configuration status for each mapped building or facility

position_from_wifi_scan

Returns estimated coordinates with uncertainty radius. Use for server-side positioning when mobile SDK integration is not feasible. Determine indoor position from a Wi-Fi access point scan using the IndoorAtlas Positioning API, submitting observed signal strengths to receive a calculated latitude, longitude, floor level, and accuracy estimate

trigger_map_generation

This is a critical step — positioning will not work on a floor until map generation completes successfully. The process is asynchronous and may take several minutes depending on floor plan complexity. Trigger the IndoorAtlas radio map generation process for a specific floor plan, initiating the server-side computation that creates the positioning model from fingerprint data and floor plan geometry

upload_floorplan_geojson

After upload, trigger map generation to enable positioning on this floor. Upload a new floor plan to an IndoorAtlas venue as a GeoJSON document, geo-referencing the indoor map image to real-world coordinates for accurate positioning overlay

Example Prompts for IndoorAtlas (Indoor Positioning) in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with IndoorAtlas (Indoor Positioning) immediately.

"List all indoor venues in my IndoorAtlas account"

"Check the calibration paths for the 3rd floor of the 'Retail Mall'"

"List the most recent positioning sessions recorded today"

Troubleshooting IndoorAtlas (Indoor Positioning) MCP Server with CrewAI

Common issues when connecting IndoorAtlas (Indoor Positioning) to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

IndoorAtlas (Indoor Positioning) + CrewAI FAQ

Common questions about integrating IndoorAtlas (Indoor Positioning) MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.