How to Use the Zenoti MCP in LangChain

Q: How does Zenoti help me manage appointments using LangChain?

You use the listappointments tool, filtering by date to see a full day's schedule. The agent can then cross-reference this with specific therapist availability found via listtherapists. It gives you a clear picture of who's working when.

Q: Can LangChain help me understand guest spending patterns at Zenoti?

Absolutely. You can combine searchguests (to get visit history) with listinvoices and getguestloyalty. The agent sequences these calls to build a complete picture of how much a client spends over time.

Q: Does Zenoti support managing multiple locations with LangChain?

Yes. You first call listcenters to pull all location details—addresses and operating hours. Then you can run other tools like listemployees to see which staff are active at a specific branch.

Q: How do I check pricing for services with Zenoti using LangChain?

You simply call the listservices tool. This immediately returns all available service details, including their price and estimated duration, so you can build your logic around specific offerings.

Build complex business logic chains using the Zenoti MCP Server with LangChain.

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Connect Zenoti MCP to LangChain

Create your Vinkius account to connect Zenoti to LangChain 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 Zenoti with LangChain

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Multi-Step Staffing and Scheduling Logic via LangChain

Need to figure out who can see a client today? Start by running `search_guests` to get the profile. Then, check which therapists are available using `list_therapists`. Finally, combine that with `get_appointment` details to confirm availability and booking conflicts. This creates complex decision paths. Your agent builds these chains automatically. It decides if it needs the guest's loyalty points (`get_guest_loyalty`) first before checking for a slot using `list_appointments`. The output of one tool feeds directly into the next step.

Managing Client Journeys with MCP Server

Tracking a client from their first visit to their membership renewal is simple. Use `get_guest` for core profile info, then check out what services they bought using `list_services`. If they bought a package, the agent pulls that data via `list_packages`. It handles the whole flow. It's all about connecting the dots: Checking gift card balances with `list_gift_cards` and making sure the right center is used for the transaction via `get_center`. This makes your multi-step reasoning pipelines tight.

Financial Operations Chains for LangChain

Handling revenue requires multiple tool calls. The agent first uses `list_invoices` to check past earnings within a date range. Then, it can verify membership status using `list_memberships`. You don't just get a list; you build the sequence of checks needed for accurate accounting. The chain finishes by listing services or packages with `list_services` and ensuring the correct pricing was applied on the final invoice record.

Setup guide

Set up Zenoti MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Zenoti tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "zenoti-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent Zenoti transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Zenoti. 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 Zenoti MCP in LangChain

You use the `list_appointments` tool, filtering by date to see a full day's schedule. The agent can then cross-reference this with specific therapist availability found via `list_therapists`. It gives you a clear picture of who's working when.

Absolutely. You can combine `search_guests` (to get visit history) with `list_invoices` and `get_guest_loyalty`. The agent sequences these calls to build a complete picture of how much a client spends over time.

The MCP Server touches sensitive financial transaction data, including invoice records and guest loyalty points. All communication is handled via the secure Vinkius endpoint token, ensuring your AI client controls the flow.

Yes. You first call `list_centers` to pull all location details—addresses and operating hours. Then you can run other tools like `list_employees` to see which staff are active at a specific branch.

You simply call the `list_services` tool. This immediately returns all available service details, including their price and estimated duration, so you can build your logic around specific offerings.

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