How to Use the SevenRooms MCP in Pydantic AI

Q: Can I use Pydantic AI to manage multiple SevenRooms venues?

Yes. You call listvenues first to get all locations, and then you can pass those validated IDs into other tools, like checking availability across the whole group.

Q: What data does Pydantic AI touch regarding guest profiles?

The server accesses and validates your guest profile information. This includes structured details about individuals using searchguests, ensuring consistency in all agent outputs.

Q: If I use Pydantic AI, can I check wait times?

You run checkavailability. The resulting metrics—open tables and current wait estimates—are strictly typed. This gives you predictable data for your workflow logic.

Q: How do I manage SevenRooms events using Pydantic AI?

Use the listevents tool to pull event details. The output format is guaranteed, which means when processing capacity or pricing data in your agent, you never have to worry about unexpected field names.

Type-safe SevenRooms operations using Pydantic AI for guaranteed data correctness.

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Setup SevenRooms with Pydantic AI

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SevenRooms MCP Server: Guest Profile Retrieval

When you need a guest's full profile, `get_guest` pulls the details and validates them against your Pydantic models. If the API sends unexpected data, the agent fails loud, preventing bad info from entering your system. You can also use `search_guests` to find specific people by name, but the response is always typed correctly—no hallucinated fields, just clean, actionable data.

Checking SevenRooms Availability

Need to know if tables are open? Run `check_availability`. The results—open spots, wait times, and room options—are validated against a strict schema. You always know the format you're getting. This is reliable for checking both general availability and specific dining room capacities.

Listing SevenRooms Operations

To see tonight's bookings, call `list_reservations`. The data structure of reservations—who, when, how many—is always guaranteed by Pydantic. It’s built for critical workflow steps. Need to know about a multi-location group? Use `list_venues` to get a validated list of every physical location under SevenRooms.

Setup guide

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

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

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

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Common questions about SevenRooms MCP in Pydantic AI

The core benefit is runtime validation. Every response from the MCP Server, whether it's a reservation or a guest profile, must match your defined Pydantic schema. This stops silent data corruption.

Yes. You call `list_venues` first to get all locations, and then you can pass those validated IDs into other tools, like checking availability across the whole group.

The server accesses and validates your `guest` profile information. This includes structured details about individuals using `search_guests`, ensuring consistency in all agent outputs.

You run `check_availability`. The resulting metrics—open tables and current wait estimates—are strictly typed. This gives you predictable data for your workflow logic.

Use the `list_events` tool to pull event details. The output format is guaranteed, which means when processing capacity or pricing data in your agent, you never have to worry about unexpected field names.

Use it with your favorite AI tools

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