How to Use the HQBeds MCP in Pydantic AI

Build type-safe booking logic with Pydantic AI. HQBeds integration ensures your agent handles reservations without silent failures.

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Connect HQBeds MCP to Pydantic AI

Create your Vinkius account to connect HQBeds 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 HQBeds with Pydantic AI

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Validate reservation data in Pydantic AI

Every response from `get_reservation` or `get_guest` is checked against Pydantic models at runtime. If the API returns unexpected fields, the agent catches the error immediately instead of passing bad data through your system. This strict validation ensures that your `create_reservation` calls always contain the correct parameters. You won't deal with hallucinated data or malformed booking requests.

Type-safe room tracking for Pydantic AI

Use `list_rooms` to populate your agent's knowledge base with confirmed property data. Pydantic AI enforces the schema for these room objects, keeping your agent's internal state reliable. When your agent checks `list_availability`, the results are parsed directly into your defined objects. This prevents logic errors when your agent decides which rooms are actually open for booking.

Controlled operations via Pydantic AI

The MCPToolset ensures all interactions with HQBeds are strictly typed. You define the structure, and the agent follows it, using `list_reservations` to pull only the data you have explicitly modeled. This keeps your agent's operations predictable and easy to debug. If a tool response deviates from your schema, the agent halts, protecting your production environment from corrupted state.

Setup guide

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

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

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

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Common questions about HQBeds MCP in Pydantic AI

Install the slim package and use MCPToolset to point to your server URL. Pass it into your agent's toolsets list for runtime validation.

The agent will raise a validation error immediately. This prevents your system from using corrupt data and allows you to catch issues at the source.

Yes. It supports both Streamable HTTP and SSE transports, giving you flexibility in how you connect your agent to the server.

Absolutely. Because Pydantic AI is model-agnostic, the server works fine with local models as long as they follow the toolset's typed responses.

All access is protected by your endpoint token. The server treats every request as a discrete, authenticated operation, keeping your guest records isolated.

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