How to Use the ChargeOver MCP in Pydantic AI

Q: Does Pydantic AI validate the data returned by ChargeOver?

Yes, every response from tools like getinvoicedetails is validated against strict schemas. If ChargeOver returns a field format that doesn't match your Pydantic model, the agent catches it instantly.

Validate ChargeOver invoices and subscriptions at runtime using type-safe Pydantic AI agents.

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Connect ChargeOver MCP to Pydantic AI

Create your Vinkius account to connect ChargeOver 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 ChargeOver with Pydantic AI

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Type-Safe Subscription Tracking in Pydantic AI

Stop worrying about silent API format changes breaking your financial code. Your agent calls `list_billing_subscriptions` and validates every field against your Pydantic models before the data ever reaches your application logic. If a subscription field contains unexpected data, the framework raises a validation error immediately. This prevents bad customer data retrieved from `get_customer_details` from corrupting your internal databases.

Assert Invoice Accuracy with the MCP Server

Run strict checks on all outgoing bills. The agent uses `list_chargeover_invoices` to gather recent billing records and confirms that the tax rates and totals match your internal bookkeeping rules. It verifies specific line items by calling `get_invoice_details`. Because the response maps directly to typed Python classes, your code can safely calculate outstanding balances without type-casting surprises.

Audit Transactions Without Silent Failures

Keep your ledger clean by validating every payment. The agent executes `list_billing_transactions` to pull recent credit card and ACH payments, ensuring each record conforms to your expected schema. It cross-references these payments against sales estimates using `list_billing_quotes`. If the agent detects a mismatch, it queries `get_chargeover_account` to verify the merchant configuration before throwing a clean, traceable exception.

Setup guide

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

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

result = await agent.run("List recent ChargeOver 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 ChargeOver. 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 ChargeOver MCP in Pydantic AI

Use the MCPToolset class pointing to your Vinkius server URL and pass it to your Agent's toolsets list. This registers all tools, such as `list_chargeover_customers`, with full runtime type validation.

Yes, every response from tools like `get_invoice_details` is validated against strict schemas. If ChargeOver returns a field format that doesn't match your Pydantic model, the agent catches it instantly.

You can connect using either Streamable HTTP or SSE transports. Simply supply your secure Vinkius endpoint token to the MCPToolset constructor to establish a connection.

Yes, Pydantic AI is model-agnostic. You can run local models or commercial APIs to process your billing data using tools like `list_billing_quotes` as long as the model supports tool calling.

Vinkius processes all requests inside ephemeral V8 isolates that spin down immediately after execution. Your subscriber records, invoice details, and billing data are never cached, logged, or exposed to external networks.

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