Zenoti MCP Server for LangChain 14 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Zenoti through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"zenoti": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Zenoti, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Zenoti MCP Server
Connect your Zenoti organization to any AI agent and manage your spa, salon, or medspa enterprise through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Zenoti through native MCP adapters. Connect 14 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
What you can do
- Guests — Search guests, view profiles, preferences, allergies, and loyalty points
- Appointments — Browse bookings with therapist, room, service, and payment status
- Services — List all spa/salon services with pricing, duration, and categories
- Therapists — View providers with specialties, ratings, and availability
- Invoices — Track revenue, sales, tips, and payment breakdowns
- Memberships — Manage membership tiers: basic, premium, VIP, couples
- Packages — Browse bundled services: couples massage, day spa, bridal
- Gift Cards — Track gift card balances, sales, and redemptions
- Centers — Manage multi-location operations across your enterprise
The Zenoti MCP Server exposes 14 tools through the Vinkius. Connect it to LangChain 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 Zenoti to LangChain via MCP
Follow these steps to integrate the Zenoti MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 14 tools from Zenoti via MCP
Why Use LangChain with the Zenoti MCP Server
LangChain provides unique advantages when paired with Zenoti through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Zenoti MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Zenoti queries for multi-turn workflows
Zenoti + LangChain Use Cases
Practical scenarios where LangChain combined with the Zenoti MCP Server delivers measurable value.
RAG with live data: combine Zenoti tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Zenoti, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Zenoti tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Zenoti tool call, measure latency, and optimize your agent's performance
Zenoti MCP Tools for LangChain (14)
These 14 tools become available when you connect Zenoti to LangChain via MCP:
get_appointment
Get appointment details
get_center
Get center details
get_guest
Get guest profile
get_guest_loyalty
Get guest loyalty points
list_appointments
Filter by date to see a specific day. List spa/salon appointments
list_centers
Includes name, address, timezone, and operating hours. Essential for multi-location spa chains like Massage Envy. List spa/salon locations
list_employees
Includes role, schedule, payroll info, and commission structure. List all employees
list_gift_cards
Filter by guest to see a specific person's cards. List gift cards
list_invoices
Filter by date range for revenue analysis. List sales and invoices
list_memberships
Shows pricing, included services, visit limits, and perks. List membership plans
list_packages
Shows included services and pricing. List service packages
list_services
Includes pricing, duration, category, and required room type. List spa/salon services
list_therapists
List therapists and providers
search_guests
Returns profile, visit history, loyalty points, preferred therapist, and product preferences. Search spa/salon guests
Example Prompts for Zenoti in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Zenoti immediately.
"Show today's appointments at the downtown center."
"Find the profile for guest Maria Gonzalez and check her loyalty points."
"What is the total revenue collected across all centers today?"
Troubleshooting Zenoti MCP Server with LangChain
Common issues when connecting Zenoti to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersZenoti + LangChain FAQ
Common questions about integrating Zenoti MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect Zenoti with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Zenoti to LangChain
Get your token, paste the configuration, and start using 14 tools in under 2 minutes. No API key management needed.