How to Use the Eventzilla MCP in Pydantic AI

Bring type-safe Eventzilla data into Pydantic AI. Validate every registration and order response against your schema at runtime.

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Connect Eventzilla MCP to Pydantic AI

Create your Vinkius account to connect Eventzilla 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 Eventzilla with Pydantic AI

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Type-safe event performance data

Use `get_event_performance_summary` to fetch metrics that Pydantic AI validates against your models. If the data structure changes, your agent fails safely instead of hallucinating. This ensures your event reporting is always accurate. It is the safest way to integrate financial and attendance data into your agentic workflow.

Verify future event schedules

The `list_future_scheduled_events` tool provides a strictly typed list of upcoming events. Your agent uses this to plan outreach without guessing the data format. Pydantic AI enforces your schema on every response. This eliminates silent errors when you are managing complex event pipelines.

Retrieve account metadata reliably

Use `get_eventzilla_account_metadata` to check your current API limits. Your agent uses this to adjust its polling rate before it hits a wall. It keeps your integration running smoothly. You get the exact configuration details you need to manage your Eventzilla account programmatically.

Setup guide

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

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

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

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Common questions about Eventzilla MCP in Pydantic AI

Every response from the MCP server is validated against your Pydantic models. If the API returns unexpected fields, the agent stops immediately to prevent errors.

Yes, the server is model-agnostic. Pydantic AI ensures that whether you use a local model or a cloud service, the data remains typed and safe.

You just need the pydantic-ai-slim package. Simply initialize the MCPToolset with your endpoint and add it to your agent's toolset list.

Pydantic AI will catch the validation error during the next execution. You will see exactly which field failed, allowing for quick adjustments to your models.

The server acts as a strictly typed gateway. It only parses the specific transaction order fields you define in your models, minimizing exposure.

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