How to Use the CellarPass MCP in Pydantic AI

Bring type-safe CellarPass MCP integrations to Pydantic AI. Validate reservation data and guest histories before your agent acts.

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Connect CellarPass MCP to Pydantic AI

Create your Vinkius account to connect CellarPass 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 CellarPass with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Type-safe Pydantic AI bookings

Silent failures ruin production systems. When your agent calls `list_reservations` to see who is coming today, the framework validates the response against strict models. If the API returns a string instead of an expected integer for party size, the system throws an error immediately. You never have to worry about hallucinated fields crashing your downstream logic. Connect the MCP Server using the unified MCPToolset class, and your agent will enforce correctness every time it fetches tasting room data.

Fetch verified guest profiles

Front-of-house staff need accurate VIP status and spending history. The `search_guests` tool pulls this exact information, so your agent bases its hospitality recommendations on real data rather than guesses. Because this framework is model-agnostic, you can use Claude to parse complex guest preferences or switch to a cheaper local model for simple lookups. The tools work exactly the same way regardless of the LLM powering the logic.

Check capacity without errors

Booking coordination demands precision. Your agent relies on `check_availability` to confirm open slots before suggesting a time to a customer. It also uses `get_venue` to verify the location's specific operating hours. Combine these with `list_experiences` to give the user a complete, validated list of tasting options. They see the exact pricing, duration, and capacity without any AI-generated guesswork muddying the details.

Setup guide

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

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

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

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Common questions about CellarPass MCP in Pydantic AI

Install the slim package with MCP support. Create an MCPToolset pointing to your Vinkius URL and add it to your agent's toolsets list.

No, that class is deprecated. Always use the unified MCPToolset approach for external HTTP endpoints.

The framework throws a loud validation error. Your agent stops execution rather than passing corrupted data to your users.

Yes. The toolset integrates perfectly whether you are running a massive cloud model or a small local instance for simple queries.

We manage the authentication layer securely. Every request for spending history or visit data happens in an ephemeral, isolated environment that shuts down immediately after the transaction.

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