How to Use the Virtuagym MCP in CrewAI

Q: Can CrewAI automate Virtuagym reporting of staff and programs?

Yes. You assign separate roles for listing trainers (listtrainers) and workout plans (listworkoutplans). The crew handles the sequential execution, compiling all lists into a unified output.

Automate Virtuagym operations with specialized agents using CrewAI.

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Connect Virtuagym MCP to CrewAI

Create your Vinkius account to connect Virtuagym 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 Virtuagym with CrewAI

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Claude

Perplexity

Analyze member needs and suggest programs.

Building a consultation tool? You can deploy multiple agents. One agent researches the client's data via `search_members`, while another analyzes that output against available options using `list_nutrition_plans`. The third writes up the recommendation. The strength here is specialization: you assign roles—Research Agent, Analysis Agent—to handle complex, multi-step operations without needing human intervention.

Manage and report on gym operational resources.

Need a full inventory? You can deploy agents to query all available staff using `list_trainers` and all programs via `list_workout_plans`. The system compiles these disparate lists into a single, cohesive report. The crew model lets you assign an agent to 'Monitor' the status of all resources and another to 'Act' on outdated information.

Handle daily club information reporting.

For daily operations, agents can handle scheduling. One agent calls `list_activities` for general info, while a second checks `list_events` for specific class dates. The collaboration ensures both sets of data are presented together. The monitor agent watches these sessions, ensuring that if any tool call fails (like getting club details via `get_club_info`), the moderator agent is alerted immediately.

Setup guide

Set up Virtuagym 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 Virtuagym tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Virtuagym 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="Virtuagym Analyst",
        goal="Access and analyze Virtuagym data via MCP.",
        backstory="Expert analyst with direct Virtuagym access.",
        tools=mcp_tools,
    )

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

You set up a crew: one agent researches the client's fitness progress using `search_members`, and another recommends plans based on that data. The agents collaborate to deliver a full, actionable report.

Yes. You assign separate roles for listing trainers (`list_trainers`) and workout plans (`list_workout_plans`). The crew handles the sequential execution, compiling all lists into a unified output.

The server touches member profiles and check-in history. When using this MCP Server with CrewAI, you define strict boundaries for data access, ensuring agents only read what they need to perform their assigned role.

Definitely. You can create an agent dedicated to scheduling that calls `list_events` and presents the data as part of a larger, autonomous reporting cycle.

You assign agents to retrieve core details. One agent gets basic info via `get_club_info`, and another can pull activity descriptions using `list_activities`. The collaboration makes the final report comprehensive.

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