How to Use the Getpaid MCP in Pydantic AI

Q: How do I set up Getpaid in Pydantic AI?

Install the MCP toolset package and use the MCPToolset class. Provide your server URL to the constructor and pass the resulting object into your agent's toolsets list.

Q: How do I check status for Getpaid in Pydantic AI?

Call getcheckoutstatus inside your agent flow. The tool result will be checked against your Pydantic model to ensure the status matches your expected format.

Get type-safe billing operations with Getpaid and Pydantic AI for reliable payment logic.

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Connect Getpaid MCP to Pydantic AI

Create your Vinkius account to connect Getpaid 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 Getpaid with Pydantic AI

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Runtime validation for Getpaid tools

Pydantic AI validates every response from `get_payment_details` against your schema. If the API returns malformed data, the agent stops before it can act on bad info. It forces correctness at every step. You don't have to worry about silent errors affecting your billing records.

Type-safe subscription management with Getpaid

Use `list_billing_subscriptions` to pull your recurring revenue data. The agent receives a validated list, so you can trust the structure of every plan and status update. It makes your financial agent code much cleaner. You define the model, and the server ensures the data matches.

Automated refund safety with Getpaid

Process refunds via `refund_billing_payment` with full type checking. Your agent validates the input parameters against your local models before it hits the gateway. This prevents accidental calls with missing data. It's the standard for building production-ready financial agents.

Setup guide

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

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

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

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Common questions about Getpaid MCP in Pydantic AI

Install the MCP toolset package and use the `MCPToolset` class. Provide your server URL to the constructor and pass the resulting object into your agent's `toolsets` list.

Yes. If the API returns an error or unexpected field, Pydantic AI will raise a validation error. This prevents your agent from hallucinating or processing corrupt payment data.

Absolutely. Since Pydantic AI is model-agnostic, you can connect this MCP Server to local models while keeping the same strict type-safety for all billing operations.

Call `get_checkout_status` inside your agent flow. The tool result will be checked against your Pydantic model to ensure the status matches your expected format.

The server maintains a private connection to your Getpaid account. By using Pydantic models to define exactly what data the agent can access, you ensure that only the fields you explicitly trust are ever processed by the agent.

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