How is WellnessLiving different from Mindbody?

WellnessLiving offers similar features at a lower price point with no hidden fees. It's particularly popular among martial arts schools, dance studios, and music schools. It also provides a PHP SDK on GitHub for custom development.

WellnessLiving MCP Server for LlamaIndex 9 tools — connect in under 2 minutes

Name: WellnessLiving
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 9 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add WellnessLiving as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to WellnessLiving. "
            "You have 9 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in WellnessLiving?"
    )
    print(response)

asyncio.run(main())

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About WellnessLiving MCP Server

Connect your WellnessLiving studio to any AI agent and manage your fitness business through natural conversation.

LlamaIndex agents combine WellnessLiving tool responses with indexed documents for comprehensive, grounded answers. Connect 9 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Schedule — View daily class schedule with instructors, capacity, and enrollment
Clients — Search members, view profiles, credits, and attendance history
Staff — List instructors and trainers with roles and schedules
Memberships — Browse all promotion/membership plans with pricing
Locations — Manage multi-location studios with addresses and hours
Reports — Access business reports: attendance, revenue, retention
Business — View account profile and configuration

The WellnessLiving MCP Server exposes 9 tools through the Vinkius. Connect it to LlamaIndex 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 WellnessLiving to LlamaIndex via MCP

Follow these steps to integrate the WellnessLiving MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

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 9 tools from WellnessLiving

Why Use LlamaIndex with the WellnessLiving MCP Server

LlamaIndex provides unique advantages when paired with WellnessLiving through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine WellnessLiving tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain WellnessLiving tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query WellnessLiving, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what WellnessLiving tools were called, what data was returned, and how it influenced the final answer

WellnessLiving + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the WellnessLiving MCP Server delivers measurable value.

Hybrid search: combine WellnessLiving real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query WellnessLiving to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying WellnessLiving for fresh data

Analytical workflows: chain WellnessLiving queries with LlamaIndex's data connectors to build multi-source analytical reports

WellnessLiving MCP Tools for LlamaIndex (9)

These 9 tools become available when you connect WellnessLiving to LlamaIndex via MCP:

get_business_info

Get business info

get_client

Get client profile

get_report

Reports include attendance, revenue, retention metrics. Get business report

list_locations

List business locations

list_memberships

List membership plans

list_schedule

List class schedule

list_services

List class types/services

list_staff

List staff members

search_clients

Returns profile, memberships, and visit history. Search clients/members

Example Prompts for WellnessLiving in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with WellnessLiving immediately.

"Show today's class schedule."

"Check David Chen's attendance history and remaining class credits."

"Generate a summary of yesterday's revenue and attendance for the Uptown studio."

Troubleshooting WellnessLiving MCP Server with LlamaIndex

Common issues when connecting WellnessLiving to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

WellnessLiving + LlamaIndex FAQ

Common questions about integrating WellnessLiving MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query WellnessLiving tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.