How to Use the Zenoti MCP in CrewAI

Q: Can I use Zenoti service data with CrewAI?

You absolutely can. You'll deploy an agent to call listservices and another agent dedicated solely to calling listpackages. The agents share memory, allowing them to reference each other’s findings.

Run autonomous Zenoti operations using CrewAI multi-agent teams.

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Connect Zenoti MCP to CrewAI

Create your Vinkius account to connect Zenoti to CrewAI 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 CrewAI

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Perplexity

Coordinating guest search and loyalty checks

Set up a team where Agent A researches the client's details by calling `search_guests`. Then, Agent B analyzes that data and calls `get_guest_loyalty` to determine the reward status. The resulting information is passed to a final moderator agent. This specialization means you don't get one huge blob of text; you get targeted analysis on the client's history.

Autonomous scheduling and staff assignment

Use three agents: an Availability Agent calls `list_appointments` to check open slots. A Staffing Agent calls `list_therapists` to confirm provider availability. Finally, a Booking Agent coordinates both pieces of data to suggest the best time. This hierarchical execution mimics how real managers plan shifts.

Processing inventory and sales records

Build a crew where one agent pulls all current services using `list_services`. A second agent calculates potential revenue by calling `list_packages` to see included value. The final agent summarizes the profitability. This separation of duties ensures that pricing rules are applied correctly before reporting.

Setup guide

Set up Zenoti MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Zenoti tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Zenoti Analyst",
    goal="Access and analyze Zenoti data via MCP.",
    backstory="Expert analyst with direct Zenoti access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Zenoti transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Zenoti Analyst",
        goal="Access and analyze Zenoti data via MCP.",
        backstory="Expert analyst with direct Zenoti access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Zenoti transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

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 CrewAI

CrewAI lets you assign different agents roles. One agent can call `search_guests` to get the profile, and a second agent handles checking loyalty points via `get_guest_loyalty`, keeping tasks specialized.

You absolutely can. You'll deploy an agent to call `list_services` and another agent dedicated solely to calling `list_packages`. The agents share memory, allowing them to reference each other’s findings.

You can set up a team: Agent A pulls the raw sales figures using `list_invoices`. Agent B then filters that data by date range, and a third agent generates the final executive summary.

Yes. Because it's a multi-agent setup, you can add more specialized agents (like one for memberships or gift cards) without rebuilding the core workflow structure.

This server touches Guest Profile. The shared memory feature is key here, as it ensures all agents work from a consistent and secure view of the client's profile data.

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