Can I see the reason why a transaction failed?

Yes! Use the `list_champ_transactions` tool. The agent will retrieve recent transactions including the decline reason and response from the payment gateway.

How do I list my latest leads from a specific campaign?

Use the `list_champ_leads` tool. You can then ask the agent to filter the results by campaign name or ID to see who signed up recently.

Where do I find my API Login ID and Password?

Log in to Checkout Champ and go to the 'Settings' or 'Admin' section to manage API Users. You can create a dedicated API user and get the credentials there.

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

Name: Checkout Champ
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 Checkout Champ through the 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 Checkout Champ "
            "(8 tools)."
        ),
    )

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

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Checkout Champ MCP Server

Connect your Checkout Champ account to any AI agent and take full control of your e-commerce CRM and order management through natural conversation. Streamline your sales funnel and customer logistics.

Pydantic AI validates every Checkout Champ tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through the 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

Order Oversight — List and retrieve details for all customer orders and fulfillment statuses natively
Lead Intelligence — Access and monitor captured leads to understand your sales pipeline flawlessly
Transaction Auditing — List and review financial transactions and payment gateway responses securely
Customer Management — Access detailed customer profiles and their complete interaction history flawlessly
Campaign Tracking — List configured marketing campaigns and retrieve their performance metadata flawlessly
Product Logistics — Access your product catalog and retrieve master data directly within your workspace

The Checkout Champ 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 Checkout Champ to Pydantic AI via MCP

Follow these steps to integrate the Checkout Champ 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 Checkout Champ with type-safe schemas

Why Use Pydantic AI with the Checkout Champ MCP Server

Pydantic AI provides unique advantages when paired with Checkout Champ 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 Checkout Champ integration code

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

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

Checkout Champ + Pydantic AI Use Cases

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

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

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

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

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

Checkout Champ MCP Tools for Pydantic AI (8)

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

get_champ_customer_details

Get detailed information for a specific customer

get_champ_order_details

Get detailed information for a specific order

list_champ_campaigns

List configured marketing campaigns

list_champ_customers

List customers in the CRM

list_champ_leads

List captured leads

list_champ_orders

List orders from Checkout Champ

list_champ_products

List products in the catalog

list_champ_transactions

List recent financial transactions

Example Prompts for Checkout Champ in Pydantic AI

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

"Show me the last 5 orders in Checkout Champ."

"What is my total sales volume for today?"

"List all active campaigns."

Troubleshooting Checkout Champ MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Checkout Champ + Pydantic AI FAQ

Common questions about integrating Checkout Champ 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 Checkout Champ MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.