How do I get an OnPay API Token?

Log in to your OnPay management panel, and navigate to Settings > API to generate a static API token.

OnPay MCP Server for Pydantic AI 8 tools — connect in under 2 minutes

Name: OnPay
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 8 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect OnPay through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to OnPay "
            "(8 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in OnPay?"
    )
    print(result.data)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About OnPay MCP Server

Connect your OnPay account to empower your AI agents with payroll and HR capabilities. This server provides programmatic access to the OnPay API, allowing for seamless management of employee profiles, payment transactions, and recurring pay schedules.

Pydantic AI validates every OnPay tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Employee Directory — List and inspect complete employee profiles and records
Transaction Monitoring — Access historical payment transactions and statuses in real-time
Payroll Orchestration — Monitor upcoming pay schedules and cycles for your company
Subscription Tracking — View active and past subscriptions managed through the platform
Accounting Integration — Retrieve item codes and company settings for financial reconciliation

The OnPay MCP Server exposes 8 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect OnPay to Pydantic AI via MCP

Follow these steps to integrate the OnPay MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 8 tools from OnPay with type-safe schemas

Why Use Pydantic AI with the OnPay MCP Server

Pydantic AI provides unique advantages when paired with OnPay through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your OnPay integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your OnPay connection logic from agent behavior for testable, maintainable code

OnPay + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the OnPay MCP Server delivers measurable value.

Type-safe data pipelines: query OnPay with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple OnPay tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query OnPay and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock OnPay responses and write comprehensive agent tests

OnPay MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect OnPay to Pydantic AI via MCP:

get_company_info

Get general company information

get_employee

Get employee details

get_transaction

Get transaction details

list_employees

List all employees

list_item_codes

List accounting item codes

list_pay_schedules

List payroll schedules

list_subscriptions

List subscriptions

list_transactions

List payment transactions

Example Prompts for OnPay in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with OnPay immediately.

"List all active employees."

Troubleshooting OnPay MCP Server with Pydantic AI

Common issues when connecting OnPay to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

OnPay + Pydantic AI FAQ

Common questions about integrating OnPay MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your OnPay MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.