How to Use the Luma MCP in Pydantic AI

Build type-safe event automation with Pydantic AI and the Luma MCP Server.

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Connect Luma MCP to Pydantic AI

Create your Vinkius account to connect Luma 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 Luma with Pydantic AI

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Claude

Perplexity

Validate your Luma operations

Pydantic AI checks every response from the Luma MCP Server against your predefined schemas. If `list_event_guests` returns malformed data, your agent stops immediately. This prevents silent errors in your event pipeline. You get absolute confidence that your agent is working with valid attendee records.

Strict event guest handling

Your agent uses `add_guests_to_event` with full type safety. Pydantic AI rejects any payload that doesn't match the required structure for your Luma account. This keeps your guest lists clean. Your agent verifies the output of every tool before it proceeds to the next step of your workflow.

Type-safe calendar listing

You query your schedule using `list_events` and get back structured, validated Python objects. Your agent works with these models directly. This eliminates the need for manual parsing of JSON responses. You focus on your agent logic while Pydantic AI handles the data integrity.

Setup guide

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

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

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

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Common questions about Luma MCP in Pydantic AI

Install the slim MCP package and use the MCPToolset class with your Vinkius endpoint. This provides a unified interface for your agent to execute tools.

Your agent raises a validation error instantly. This prevents corrupted data from entering your system and alerts you to the discrepancy.

Absolutely. Because every tool response is validated at runtime, it provides the reliability needed for professional event management systems.

You define a model for your subscribers and the MCP toolset maps the response to it. This ensures your agent always knows the exact shape of your data.

Yes. The framework is model-agnostic, so you can connect your own models to the Luma MCP Server while keeping your data type-safe.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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