How to Use the Hubilo MCP in Pydantic AI

Q: How do I initialize the Hubilo toolset in Pydantic AI?

Install the library with the MCP extra and use the MCPToolset class pointing to your Vinkius HTTP URL. Pass this toolset instance inside the toolsets list when defining your Pydantic AI agent.

Get strict type safety and runtime validation for your Hubilo event data using Pydantic AI agent frameworks.

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Connect Hubilo MCP to Pydantic AI

Create your Vinkius account to connect Hubilo 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 Hubilo with Pydantic AI

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Prevent silent failures with runtime type validation

If an API payload changes, generic agents break silently or hallucinate fields. Pydantic AI forces every response from `list_attendees` or `list_orders` through strict validation models. If Hubilo returns a null field where a string belongs, the agent fails loudly and immediately. This guarantees data integrity. Your downstream code can trust that data from `get_event_details` or `list_tickets` matches your expected schemas exactly, eliminating runtime crashes in production.

Build model-agnostic event agents with Pydantic AI

Don't get locked into one LLM provider. Pydantic AI lets you swap models while keeping your Hubilo tools intact. You can run cheap models for simple tasks like pulling speaker lists with `list_speakers` and switch to advanced models for complex logistics. No matter if you query `list_sessions` or analyze exhibitor booths with `get_booth_analytics`, the underlying MCP Server setup remains identical across different models.

Run structured workflows for event organizers

Managing events requires precise coordination. Use this MCP Server to pull organizer profiles via `get_organizer_profile` and list categories with `list_booth_categories` using clean, structured Python code. No messy dictionary parsing required. The framework treats every tool call as a typed object. When searching live events with `list_events`, you get fully validated Pydantic models back, making it easy to build reliable automation scripts.

Setup guide

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

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

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

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Common questions about Hubilo MCP in Pydantic AI

Install the library with the MCP extra and use the `MCPToolset` class pointing to your Vinkius HTTP URL. Pass this toolset instance inside the `toolsets` list when defining your Pydantic AI agent.

The agent will raise a validation error instantly instead of passing corrupted data to the LLM. This ensures that tools like `list_orders` or `get_booth_analytics` never feed malformed inputs into your agent's reasoning loop.

Yes, because Pydantic AI is model-agnostic. You can connect your MCP Server to local models running via Ollama or vLLM, allowing you to process `list_attendees` data locally.

Yes, the framework supports both Streamable HTTP and SSE transports. You just need to run the server externally and configure the `MCPToolset` with the appropriate endpoint.

Your ticket prices, order histories, and attendee names are validated in memory and never written to disk. Vinkius's zero-trust MCP sandbox ensures that calls to `list_tickets` or `list_orders` are executed ephemerally, keeping your event financial data isolated.

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