How to Use the Zenoti MCP in Pydantic AI

Q: Is the Pydantic AI suitable for complex Zenoti employee records?

Definitely. When calling listemployees, the resulting object must conform to a defined schema, guaranteeing that payroll and commission data are always structured correctly for your downstream services.

Q: Does Pydantic AI handle multiple financial record types from Zenoti?

Yes. It validates both transactional records (listinvoices) and asset records (listgiftcards), ensuring that whether you're checking a balance or analyzing sales, the data integrity is preserved.

Build reliable agents that validate Zenoti operations using Pydantic AI's type safety.

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Connect Zenoti MCP to Pydantic AI

Create your Vinkius account to connect Zenoti to Pydantic AI 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 Pydantic AI

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Reliable Guest and Appointment Management with MCP Server

The primary job of the agent is handling client bookings. You can reliably get all necessary appointment details using `get_appointment`, or filter a full day's schedule with `list_appointments`. The response schema for these tools is strictly enforced. Furthermore, if you need to find a guest, `search_guests` gives you a validated structure containing profile data, loyalty points, and visit history.

Accurate Financial Tracking via MCP Server

When dealing with money, correctness matters. The Pydantic AI framework ensures that when your agent calls `list_invoices`, the resulting revenue data adheres exactly to expected formats. No unexpected fields break your production code. It handles all assets too; you can check gift card details via `list_gift_cards` or see what's included in a client's `list_memberships` plan, knowing the output structure is sound.

Structured Service and Location Cataloging

Need to build out service definitions? The agent uses tools like `list_services` or `list_packages`, ensuring that every returned field—from pricing to required duration—is typed correctly. This prevents runtime errors. It also maps physical locations, retrieving validated center data using `get_center` and listing available employees with specific role and schedule information via `list_employees`.

Setup guide

Set up Zenoti MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "zenoti-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Zenoti tools.",
)

result = await agent.run("List recent Zenoti transactions")
print(result.output)

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Common questions about Zenoti MCP in Pydantic AI

It adds a crucial layer of runtime validation. If the underlying Zenoti API sends back malformed or unexpected data, your agent fails loudly with an explicit type error instead of silently corrupting your application state.

Yes. The framework forces strict typing when calling tools like `get_appointment` and `list_appointments`. This means you know exactly what data structure to expect, making the agent reliable for scheduling tasks.

Definitely. When calling `list_employees`, the resulting object must conform to a defined schema, guaranteeing that payroll and commission data are always structured correctly for your downstream services.

Yes. It validates both transactional records (`list_invoices`) and asset records (`list_gift_cards`), ensuring that whether you're checking a balance or analyzing sales, the data integrity is preserved.

The server touches highly sensitive operational and personal records, specifically `guest profile` information. Because of its strict validation, it's excellent for ensuring that only expected data types are processed by your agent.

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