How to Use the Zenoti MCP in AutoGen

Q: How does Zenoti help me manage staffing using AutoGen?

Agents can run listemployees to get full payroll and schedule data. They debate which staff member is best suited for a role, considering their actual assigned center location found via getcenter.

Q: Does Zenoti support checking center availability with AutoGen?

You use listcenters to pull all operational hours and addresses. Agents can then cross-reference these details against staffing requirements found via listemployees to ensure optimal coverage.

Q: How do I track services with Zenoti using AutoGen?

The system uses listservices and listpackages. Agents can debate the best way to bundle these, ensuring that the combination of included services makes sense for billing and customer experience.

Drive consensus-driven decisions on Zenoti operations with AutoGen's multi-agent framework.

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Connect Zenoti MCP to AutoGen

Create your Vinkius account to connect Zenoti to AutoGen 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 AutoGen

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Perplexity

Debating Complex Service Configurations via MCP Server

Imagine an agent debate over a new service package. One agent might call `list_services` to check pricing, while another calls `list_packages` to see if the service already exists. They challenge each other's proposed rates and included services until consensus is reached. The system forces deliberation on details like required room types (from `list_services`) versus current available inventory (`get_center`). The final decision isn't just pulled; it's negotiated.

Resolving Scheduling Conflicts with AutoGen

Multiple agents can debate booking conflicts. One agent checks the availability using `list_appointments`, while a second verifies if the employee is scheduled for that time via `list_employees`. They negotiate alternative dates or staff members until the conflict is resolved. This robust conversation framework ensures no critical detail—like checking guest status with `search_guests`—is missed because an agent flagged it as necessary.

Auditing Financial Records Using AutoGen

When reviewing revenue, multiple agents can review the data. One might focus only on invoices from a date range (`list_invoices`), while another focuses purely on membership status using `list_memberships`. They challenge each other's assumptions about what constitutes 'revenue.' The outcome is an audit trail where every financial record and tool call is debated, guaranteeing the highest level of accuracy for your Zenoti operations.

Setup guide

Set up Zenoti MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Zenoti tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Zenoti_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Zenoti data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Zenoti_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Zenoti data")
    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 AutoGen

Agents can run `list_employees` to get full payroll and schedule data. They debate which staff member is best suited for a role, considering their actual assigned center location found via `get_center`.

Yes. You can initiate a conversation where agents verify the client's profile using `get_guest`, then check their points balance with `get_guest_loyalty`. This process ensures all relevant data is considered before making recommendations.

The MCP Server touches guest and billing details, including names and transaction records. The system mandates that agents respect the boundaries of this sensitive data during their debates.

You use `list_centers` to pull all operational hours and addresses. Agents can then cross-reference these details against staffing requirements found via `list_employees` to ensure optimal coverage.

The system uses `list_services` and `list_packages`. Agents can debate the best way to bundle these, ensuring that the combination of included services makes sense for billing and customer experience.

Use it with your favorite AI tools

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