How to Use the 7shifts MCP in Pydantic AI

Bring strict runtime validation to 7shifts by connecting Pydantic AI to manage restaurant schedules and employee data safely.

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Connect 7shifts MCP to Pydantic AI

Create your Vinkius account to connect 7shifts 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 7shifts with Pydantic AI

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Perplexity

Type-safe shift scheduling

Agents use `create_shift` and `list_shifts` to manage your restaurant calendar. They pass a user ID, location ID, and ISO 8601 timestamps to assign work hours. Pydantic AI validates every response against your models at runtime. If the API returns a malformed date, the framework fails loudly instead of letting silent corruption break your database.

Validated staff provisioning via MCP Server

The `create_employee` and `list_employees` tools handle roster updates. Your agent pulls the current staff list and registers new hires using basic contact information. Correctness matters more than speed here. You define the exact shape of an employee record, and the framework forces the LLM to conform to those rules before executing the HTTP request.

Model-agnostic workforce tracking

Fetch department structures with `list_locations` and check pending leave with `list_time_off`. These tools give your agent complete visibility into restaurant operations. Swap between Anthropic, OpenAI, or local models without rewriting your connection logic. Use `MCPToolset` to connect the external Vinkius server, and your agents keep working regardless of the underlying LLM.

Setup guide

Set up 7shifts 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": {
        "7shifts-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to 7shifts tools.",
)

result = await agent.run("List recent 7shifts transactions")
print(result.output)

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Common questions about 7shifts MCP in Pydantic AI

Run pip install with the slim and mcp extras. Then initialize `MCPToolset` with your Vinkius HTTP URL.

The framework checks all inputs and outputs against strict schemas. Invalid location IDs or bad timestamp formats trigger immediate validation errors.

Yes. The framework is completely model-agnostic. You plug in any supported LLM and the tooling remains identical.

The older HTTP server class is deprecated. Toolsets provide a cleaner way to group endpoints and pass them to your agent constructor.

Vinkius isolates your server in a V8 sandbox. Employee names, emails, and time-off requests pass through an ephemeral environment that retains absolutely nothing.

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