How to Use the Mollie MCP in Pydantic AI

Q: What is the setup for Mollie and Pydantic AI?

Install the pydantic-ai-slim package and initialize the MCPToolset with your server URL. You then pass this toolset to your agent instance.

Use Pydantic AI for type-safe Mollie payment operations and strict data validation.

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Connect Mollie MCP to Pydantic AI

Create your Vinkius account to connect Mollie 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 Mollie with Pydantic AI

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Strict validation for Mollie in Pydantic AI

Every response from `get_payment_details` is checked against a Pydantic model at runtime. If the API structure changes, your agent throws a validation error immediately. This prevents your agent from hallucinating fields or processing malformed data. You get a guarantee that your code receives exactly what it expects.

Type-safe refund processing with Pydantic AI

Your agent calls `list_refunds` and maps the results into typed Python objects. This ensures your logic handles refund amounts and status codes correctly. You avoid silent failures common in loosely typed systems. The agent stops if the data doesn't match the schema, protecting your financial records.

Unified tool access via MCP Server

You define the `MCPToolset` once, and your Pydantic AI agent gains access to all Mollie operations. The interaction is consistent across all seven tools. It keeps your agent code clean and focused on business logic. The server handles the transport, while your agent handles the decision-making.

Setup guide

Set up Mollie 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": {
        "mollie-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Mollie MCP in Pydantic AI

If the API output deviates from your Pydantic model, the framework raises a validation error. This forces the agent to acknowledge the data mismatch.

Yes. The framework is model-agnostic. As long as the agent supports tools, it can interact with the Mollie MCP Server regardless of the underlying model.

Install the pydantic-ai-slim package and initialize the `MCPToolset` with your server URL. You then pass this toolset to your agent instance.

You can inspect the validation errors in your agent logs. These logs show exactly which part of the Mollie response failed your model constraints.

The server transmits data over encrypted channels. Since Pydantic AI validates the structure, you can strip sensitive metadata before the agent processes the record.

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