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How to Use the ChargeOver MCP in Pydantic AI

Type-safe recurring billing automation via this MCP Server for your Pydantic AI agents.

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…and any MCP-compatible client

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Pydantic AI

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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Type-safe ChargeOver MCP Server integration

Financial data requires absolute precision. When your agent calls `create_billing_customer`, the framework checks the payload against strict schemas. Bad parameters cause the system to fail loudly. You never have to worry about silent corruption or hallucinated currency values.

Generate valid invoices every time

Draft bills using `create_billing_invoice`. Assign recurring packages with `create_subscription`. This setup is completely model-agnostic. Swap between Anthropic, OpenAI, or local weights without rewriting your billing logic.

Parse financial records reliably

Fetching data works exactly the same way. The agent runs `list_billing_transactions` and `list_billing_invoices` to pull account histories. Every response gets validated at runtime. If the API returns a string instead of a float, Pydantic catches it immediately.

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. 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. 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. 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-alternative-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)

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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Built-in savings

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Single dashboard

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place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about ChargeOver MCP in Pydantic AI

Use the unified `MCPToolset` class. Point it at your Vinkius endpoint and pass it to your agent.
Yes. Every field gets checked against your defined models before the request goes out.
Any model supported by the framework works perfectly. The tool execution remains identical.
The framework throws a validation error immediately. Your code catches the exception and prompts the user for the missing data.
We use ephemeral server instances that die after the task finishes. Your raw transaction logs and customer addresses are never stored on disk.

Start using the ChargeOver MCP today

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Built & Managed by Vinkius 30s setup 7 tools

We've already built the connector for ChargeOver. Just plug in your AI agents and start using Vinkius.

No hosting. No infrastructure. No complex setup.
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