How to Use the HelloAsso MCP in Pydantic AI

Use Pydantic AI with HelloAsso to guarantee type-safe management of your non-profit payments and order records.

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Connect HelloAsso MCP to Pydantic AI

Create your Vinkius account to connect HelloAsso 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 HelloAsso with Pydantic AI

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Type-safe payment and order retrieval

Every response from `list_payments` is validated against your Pydantic models at runtime. If the API returns a malformed structure, the agent will catch the error immediately before any faulty data reaches your logic. Use `get_payment_details` to verify specific transaction IDs with strict typing. This ensures that your agent code always handles payment amounts and status flags as expected, preventing common runtime bugs.

Strict validation of sold items

Fetch individual item sales using `list_sold_items` and let Pydantic AI enforce the schema. This guarantees that your agent only processes valid ticket or membership data from your HelloAsso campaigns. Integrate `get_order_details` into your agent loop to confirm that order data matches your internal requirements. If the API response deviates from your model, the agent halts, ensuring total data integrity.

Automated subscription and form tracking

Monitor your recurring revenue with `list_subscriptions` while maintaining strict schema compliance. This tool allows your agent to verify subscription states without risking silent data corruption. Query your campaigns with `list_forms` and rely on Pydantic AI to parse the results. You can trust that the form slugs and types are correctly typed, allowing for reliable downstream processing of your HelloAsso form data.

Setup guide

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

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

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

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Common questions about HelloAsso MCP in Pydantic AI

The framework validates every MCP response against your Pydantic models. If the data from a tool like `list_payments` does not match, the agent throws a validation error.

Absolutely. By defining your own models for the HelloAsso tool outputs, you ensure that your agent logic is shielded from unexpected API changes or bad data.

The integration uses an encrypted transport channel. Because Pydantic AI validates the payload size and structure, you also gain an extra layer of defense against unexpected data injection.

Yes. You simply point the MCPToolset to your HelloAsso server URL. The framework then handles the connection and provides the validated tools to your agent.

Pydantic AI identifies the discrepancy between the API output and your defined model. The agent stops immediately, preventing the invalid data from propagating through your system.

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