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 Pydantic AI 8 tools — connect in under 2 minutes

Name: Wellhub
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 8 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Wellhub through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

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Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Wellhub "
            "(8 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Wellhub?"
    )
    print(result.data)

asyncio.run(main())

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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.

Pydantic AI validates every Wellhub tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Wellhub MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 8 tools from Wellhub with type-safe schemas

Why Use Pydantic AI with the Wellhub MCP Server

Pydantic AI provides unique advantages when paired with Wellhub through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Wellhub integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Wellhub connection logic from agent behavior for testable, maintainable code

Wellhub + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Wellhub MCP Server delivers measurable value.

Type-safe data pipelines: query Wellhub with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Wellhub tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Wellhub and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Wellhub responses and write comprehensive agent tests

Wellhub MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect Wellhub to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

Common issues when connecting Wellhub to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Wellhub + Pydantic AI FAQ

Common questions about integrating Wellhub MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your Wellhub MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.