How to Use the EventTemple MCP in Pydantic AI

Q: How do I configure the EventTemple MCP Server with Pydantic AI?

Install pydantic-ai-slimmcp. Initialize MCPToolset using the unified HTTP approach, then pass it as toolsets=toolset to your Agent constructor.

Q: Can I pull a specific customer's details from EventTemple?

The agent will execute getcontact to fetch a single person's record. It can also use listaccounts to find the broader corporate entity they belong to.

Build type-safe hospitality agents in Pydantic AI that validate every EventTemple booking and invoice against strict schemas.

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Connect EventTemple MCP to Pydantic AI

Create your Vinkius account to connect EventTemple 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 EventTemple with Pydantic AI

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Claude

Perplexity

Type-safe EventTemple MCP Server calls

Hospitality software APIs return massive JSON payloads. When you connect the EventTemple MCP Server to Pydantic AI, every response gets validated against your predefined models at runtime. If `get_booking` returns a null value for a required check-in date, the agent fails loudly with a validation error. You never have to worry about silent corruption or a model hallucinating a banquet time that doesn't exist.

Switch models without breaking CRM logic

Pydantic AI is model-agnostic, meaning your EventTemple logic stays intact whether you run OpenAI, Anthropic, or a local model. You write the implementation once. Your agent uses `list_contacts` and `list_accounts` to pull lead information. If you swap from Claude to Gemini tomorrow, the agent still knows exactly how to format the request and parse the hospitality CRM data.

Manage venue workflow reliably

Sales coordinators rely on daily task lists to keep catering orders moving. Your agent can call `list_tasks` to pull the day's requirements and `list_events` to match them against the physical venue schedule. Because the framework enforces strict types, your Python code always receives exactly the fields it expects. When the agent pulls billing records via `list_invoices`, the financial totals are guaranteed to be formatted correctly before hitting your database.

Setup guide

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

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

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

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Common questions about EventTemple MCP in Pydantic AI

Install pydantic-ai-slim[mcp]. Initialize MCPToolset using the unified HTTP approach, then pass it as toolsets=[toolset] to your Agent constructor.

The framework throws a strict validation error. If a call to `get_event` misses required fields like the start time, the agent stops execution rather than guessing.

The agent will execute `get_contact` to fetch a single person's record. It can also use `list_accounts` to find the broader corporate entity they belong to.

Yes. Because Pydantic AI separates the LLM from the tool execution, you can point a local model at `list_bookings` and it will interact with the API exactly like a commercial model would.

This integration touches raw venue reservation schedules and billing invoices. Vinkius operates a stateless proxy model where auth is handled securely, ensuring your venue's financial data is never logged or stored on our servers.

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