How to Use the Lodgify MCP in Pydantic AI

Connect Lodgify to Pydantic AI. Ensure every booking and rate update is type-safe and validated against your schema at runtime.

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Connect Lodgify MCP to Pydantic AI

Create your Vinkius account to connect Lodgify 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 Lodgify with Pydantic AI

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Type-safe Lodgify operations

Pydantic AI validates every response from the Lodgify MCP server against your Pydantic models. If `get_booking` returns unexpected fields, the agent stops immediately to prevent data corruption. This strict validation replaces error-prone parsing with guaranteed data structures. You can trust that your agent's internal state matches the actual data returned by the API.

Strict runtime validation for Lodgify

The framework catches schema mismatches during the tool execution phase. When you call `get_rates`, the system verifies the response structure before your agent logic processes the numbers. This prevents the model from hallucinating non-existent booking fields. You maintain complete control over the data flow, ensuring that every interaction remains strictly within the bounds of your defined models.

Unified MCP toolset management

Use the MCPToolset to register your Lodgify tools into the Pydantic AI agent. It supports modern transport protocols like SSE to ensure a reliable connection to your external server. This setup provides a clean interface for your agent to query property details. By grouping these tools, you simplify the configuration and ensure your agent always has the correct definitions available.

Setup guide

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

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

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

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Common questions about Lodgify MCP in Pydantic AI

Pydantic AI enforces type safety on all Lodgify data. It prevents silent failures by validating every tool response against your expected schema before the agent uses it.

Yes, if the API returns malformed data, Pydantic AI triggers a validation error. This keeps your agent from making decisions based on corrupted or incorrect information.

Absolutely, Pydantic AI is model-agnostic. You can connect your Lodgify tools to local LLMs or cloud-based models while maintaining the same level of type-safe validation.

The server should run externally, and you point your MCPToolset to its URL. Pydantic AI then manages the communication over SSE or HTTP to keep your agent in sync.

By forcing all tool responses through Pydantic models, you strip away potential hallucinations. The agent only 'sees' the data that conforms to your strictly typed definitions.

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