How to Use the Durianpay MCP in Pydantic AI

Q: How do I initialize the Durianpay MCP Server toolset in Pydantic AI?

Install the library using pip install "pydantic-ai-slimmcp". Create an MCPToolset pointing to your Vinkius HTTP endpoint and pass it to your Agent constructor.

Q: Can I search Durianpay orders using Pydantic AI schemas?

Yes. Calling searchpaymentorders yields a structured payload that matches the Pydantic models defined for the checkout schemas, ensuring your search results are clean and parsed.

Build type-safe billing agents that validate Durianpay transactional data at runtime using Pydantic AI.

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Connect Durianpay MCP to Pydantic AI

Create your Vinkius account to connect Durianpay 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 Durianpay with Pydantic AI

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Strict runtime validation with Pydantic AI

Financial integrations cannot tolerate corrupt data. When your agent calls `get_payment_details` or `list_durianpay_payments`, Pydantic AI enforces strict schema validation on the incoming JSON payloads. If the Durianpay API returns an unexpected data format, the framework raises a validation error immediately. This prevents your agent from making decisions based on malformed or hallucinated payment records.

Type-safe checkout tracking via MCP Server

Managing checkout flows requires precise state tracking. Use `list_payment_orders` and `get_payment_order_details` to verify that checkout statuses match your internal database states. The server translates these API responses into validated Python models. Your agent can confidently process order structures without risking silent type mismatches or runtime failures.

Audit revenue health with zero hallucinations

AI models occasionally guess numbers, which is a nightmare for accounting. Use `quick_revenue_health_audit` and `get_durianpay_account_metadata` to guarantee that your agent only uses verified API data. By parsing the structured output of these tools, Pydantic AI keeps the LLM grounded. You get accurate, auditable summaries of your payment success rates and account limits.

Setup guide

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

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Durianpay. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Durianpay MCP in Pydantic AI

Install the library using `pip install "pydantic-ai-slim[mcp]"`. Create an `MCPToolset` pointing to your Vinkius HTTP endpoint and pass it to your `Agent` constructor.

The framework catches the error and raises a validation exception. If `list_failed_transactions` returns an error status, your Python code handles it cleanly before any incorrect actions occur.

Yes. Because Pydantic AI is model-agnostic, you can use these tools with OpenAI, Anthropic, Gemini, or local models while maintaining strict type safety across all of them.

Yes. Calling `search_payment_orders` yields a structured payload that matches the Pydantic models defined for the checkout schemas, ensuring your search results are clean and parsed.

We isolate all execution inside zero-trust V8 sandboxes. Your sensitive customer records and API credentials are processed in memory and never written to disk, keeping your financial pipeline secure.

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