Wellhub (formerly Gympass) is the world's largest corporate wellness platform, providing employees access to 50,000+ gyms, studios, and wellness apps in 11 countries. Companies subscribe to provide fitness benefits, and partner gyms receive Wellhub members.

Wellhub MCP Server for Google ADK 8 tools — connect in under 2 minutes

Name: Wellhub
Availability: InStock
Author: Vinkius

google.github.io/adk-docs/tools/mcp-tools

Built by Vinkius GDPR 8 Tools SDK

Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add Wellhub as an MCP tool provider through the Vinkius and your ADK agents can call every tool with full schema introspection.

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python

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import (
    StreamableHTTPConnectionParams,
)

# Your Vinkius token — get it at cloud.vinkius.com
mcp_tools = McpToolset(
    connection_params=StreamableHTTPConnectionParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    )
)

agent = Agent(
    model="gemini-2.5-pro",
    name="wellhub_agent",
    instruction=(
        "You help users interact with Wellhub "
        "using 8 available tools."
    ),
    tools=[mcp_tools],
)

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About Wellhub MCP Server

Connect your Wellhub (formerly Gympass) account to any AI agent and manage your corporate wellness program through natural conversation.

Google ADK natively supports Wellhub as an MCP tool provider — declare the Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 8 tools with Gemini's long-context reasoning for complex multi-tool workflows, with production-ready session management and evaluation built in.

What you can do

Eligibility — Check employee eligibility, manage enrollment, and verify plan access
Check-ins — Track gym visits, validate QR codes, and monitor utilization
Bookings — View class reservations made through the Wellhub app
Classes — List published classes visible to corporate members
Locations — Manage partner gym and studio locations
Partner Profile — Access business details, amenities, and network tier

The Wellhub MCP Server exposes 8 tools through the Vinkius. Connect it to Google ADK 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 Wellhub to Google ADK via MCP

Follow these steps to integrate the Wellhub MCP Server with Google ADK.

Install Google ADK

Run pip install google-adk

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Create the agent

Save the code above and integrate into your ADK workflow

Explore tools

The agent will discover 8 tools from Wellhub via MCP

Why Use Google ADK with the Wellhub MCP Server

Google ADK provides unique advantages when paired with Wellhub through the Model Context Protocol.

Google ADK natively supports MCP tool servers — declare a tool provider and the framework handles discovery, validation, and execution

Built on Gemini models, ADK provides long-context reasoning ideal for complex multi-tool workflows with Wellhub

Production-ready features like session management, evaluation, and deployment come built-in — not bolted on

Seamless integration with Google Cloud services means you can combine Wellhub tools with BigQuery, Vertex AI, and Cloud Functions

Wellhub + Google ADK Use Cases

Practical scenarios where Google ADK combined with the Wellhub MCP Server delivers measurable value.

Enterprise data agents: ADK agents query Wellhub and cross-reference results with internal databases for comprehensive analysis

Multi-modal workflows: combine Wellhub tool responses with Gemini's vision and language capabilities in a single agent

Automated compliance checks: schedule ADK agents to query Wellhub regularly and flag policy violations or configuration drift

Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including Wellhub

Wellhub MCP Tools for Google ADK (8)

These 8 tools become available when you connect Wellhub to Google ADK via MCP:

check_eligibility

Returns plan tier and access permissions. Check employee eligibility

get_partner_info

Get partner profile

list_bookings

Shows class name, time, member name, and booking status. List class bookings

list_check_ins

Shows gym name, date, time, and plan used. Essential for utilization reporting. List gym check-ins

list_classes

Shows schedule, capacity, and available spots visible to corporate members. List available classes

list_eligible_employees

Shows plan tier, activation date, and usage status. List eligible corporate employees

list_locations

List partner locations

validate_check_in

Confirms plan eligibility and records the visit. Called when a member scans their QR code at the front desk. Validate a gym check-in

Example Prompts for Wellhub in Google ADK

Ready-to-use prompts you can give your Google ADK agent to start working with Wellhub immediately.

"How many Wellhub check-ins did we have this week?"

"Verify if Alex Johnson is eligible for the Silver tier."

"List the upcoming Yoga classes available to Wellhub members at our main location."

Troubleshooting Wellhub MCP Server with Google ADK

Common issues when connecting Wellhub to Google ADK through the Vinkius, and how to resolve them.

McpToolset not found

Update: pip install --upgrade google-adk

Wellhub + Google ADK FAQ

Common questions about integrating Wellhub MCP Server with Google ADK.

How does Google ADK connect to MCP servers?

Import the MCP toolset class and pass the server URL. ADK discovers and registers all tools automatically, making them available to your agent's tool-use loop.

Can ADK agents use multiple MCP servers?

Yes. Declare multiple MCP tool providers in your agent configuration. ADK merges all tool schemas and the agent can call tools from any server in a single turn.

Which Gemini models work best with MCP tools?

Gemini 2.0 Flash and Pro models both support function calling required for MCP tools. Flash is recommended for latency-sensitive use cases, Pro for complex reasoning.