How to Use the Apaleo MCP in Pydantic AI

Use Pydantic AI for type-safe interaction with your Apaleo PMS data.

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Connect Apaleo MCP to Pydantic AI

Create your Vinkius account to connect Apaleo 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 Apaleo with Pydantic AI

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Perplexity

Validate Apaleo responses in Pydantic AI

Every call to `list_reservations` is checked against strict Pydantic models. If the PMS returns malformed data, your agent stops before a crash occurs. This ensures your agent logic is never poisoned by bad API responses. You get runtime guarantees that your code handles guest data exactly as defined.

Accounting accuracy in Pydantic AI

The `list_folios` tool retrieves billing records that must match your schema. The agent fails loudly if any field deviates from the expected structure. This is essential for audit-heavy tasks like managing invoices. You prioritize correctness over speed, and this server delivers that precision.

Managing Apaleo inventory in Pydantic AI

Use `list_room_types` to get a structured view of your inventory. Your agent processes these types with full type-safety for every property. Initialize the MCPToolset to handle the connection. The server runs externally, and your agent consumes the validated output as standard Python objects.

Setup guide

Set up Apaleo 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": {
        "apaleo-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Apaleo MCP in Pydantic AI

The server handles schema validation automatically. You just define your agent, and the MCPToolset ensures the data matches your models.

It throws a validation error immediately. This prevents your agent from hallucinating or working with corrupt hotel data.

Yes. This framework is model-agnostic and works with any LLM you choose to host.

It uses standard SSE or Streamable HTTP transports. It is built for production environments where data integrity matters most.

We use scoped tokens for every request. No guest names or folio details are ever cached or stored by the Vinkius hosting layer.

Use it with your favorite AI tools

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