How to Use the Wellhub MCP in CrewAI

Q: Can I handle location data with Wellhub using CrewAI?

Yes. You can assign a specific agent to run listlocations and then pass those coordinates or partner details to another agent for follow-up actions, like checking local bookings.

Autonomous Wellhub operations with CrewAI multi-agent systems.

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Connect Wellhub MCP to CrewAI

Create your Vinkius account to connect Wellhub 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 Wellhub with CrewAI

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Perplexity

Automated Membership Validation

You deploy a crew where Agent A researches the member's status using `check_eligibility`. Agent B then uses that result to decide if the visit is valid. This mimics human decision-making. The process automatically passes the plan tier and access permissions from one agent's memory to the next, ensuring every action is contextually aware.

Coordinated Booking Management

A specialized crew handles complex scheduling. One agent calls `list_bookings` for current reservations while another uses `list_classes` to check availability. They collaborate to recommend the best time slot. This multi-agent approach prevents single points of failure and allows you to build truly autonomous operations.

Full Operational Status Monitoring

The crew can monitor an entire site's status. One agent pulls `list_locations`, while another runs `list_check_ins` across multiple dates. A third, moderator agent aggregates this data into a single summary report. This is ideal for building dashboards that require continuous oversight without manual intervention.

Setup guide

Set up Wellhub 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 Wellhub tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Wellhub 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 →

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="Wellhub Analyst",
        goal="Access and analyze Wellhub data via MCP.",
        backstory="Expert analyst with direct Wellhub access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Wellhub 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 Wellhub. 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 Wellhub MCP in CrewAI

You define specialized roles: one agent checks eligibility, and another performs the check-in validation. The crew coordinates these steps using shared memory to achieve a full operational flow.

CrewAI allows you to simulate multiple people working together—for example, an agent researches partner details (`get_partner_info`) while another recommends available classes (`list_classes`). It's collaborative automation.

Yes. You can assign a specific agent to run `list_locations` and then pass those coordinates or partner details to another agent for follow-up actions, like checking local bookings.

This server handles member eligibility records, booking statuses, and operational check-ins. CrewAI allows multiple agents to read and synthesize this structured data for different reports.

The framework uses shared memory across the agent crew. When Agent A pulls a list of employees, that full dataset becomes available to Agent B for immediate analysis or action.

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