How to Use the Chargify MCP in Pydantic AI

Q: What's the setup process for Pydantic AI and Chargify?

After you pip install pydantic-ai-slimmcp, you create an MCPToolset instance with your Vinkius server URL. You then pass this toolset to your Pydantic AI Agent. It's a straightforward way to add validated tools.

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Connect Chargify MCP to Pydantic AI

Create your Vinkius account to connect Chargify 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 Chargify with Pydantic AI

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Create Customers Without Data Corruption

When your agent uses `create_customer`, the response from the MCP server is automatically parsed and validated against your Pydantic model. If the API ever returned an unexpected field or was missing a required one, your code would fail instantly with a clear `ValidationError`. This means no silent data corruption. Your agent can't proceed with a malformed customer object. This strictness ensures that every customer record your application works with is exactly what you expect it to be, every single time.

Execute Plan Changes with Confidence

Changing a customer's plan is a critical operation. With Pydantic AI, your agent can first call `list_catalog_products` to get a list of valid product handles, which is validated against your model. Then, when it calls `update_subscription_product`, you're certain it's using a real product handle. This approach prevents an entire class of errors. The agent is forced to operate on known-good data because Pydantic is checking the inputs and outputs at runtime. If a tool returns anything unexpected, the operation stops before a bad change is made in Chargify.

Get Subscription Data That Fits Your Code

When you ask your agent to get subscription details with `get_subscription_details`, you aren't just getting a dictionary back. You're getting a Pydantic object that has already been validated. You can access attributes like `mrr` or `state` with confidence, knowing they exist and have the correct data type. This makes your agent's code simpler and more robust. You don't need to write defensive code checking for the existence of keys or verifying data types. Pydantic AI and this MCP Server handle that contract for you.

Setup guide

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

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

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

Every response from the Chargify MCP server is automatically parsed and validated against a Pydantic model. If the data doesn't match the model's schema—for example, a string where an integer is expected—Pydantic AI raises a `ValidationError` immediately.

After you `pip install pydantic-ai-slim[mcp]`, you create an `MCPToolset` instance with your Vinkius server URL. You then pass this toolset to your Pydantic AI `Agent`. It's a straightforward way to add validated tools.

Choose it if correctness is your top priority. The runtime validation protects your application from bad data or unexpected API changes from Chargify. Your agent will fail loudly rather than corrupting data silently.

Yes. Pydantic AI is model-agnostic. You can use this Chargify toolset with agents powered by Anthropic, Gemini, local models, or any other supported LLM. The type-safe behavior works the same regardless of the model.

The security comes from strict schema enforcement. This MCP server is built to only request and return specific customer and subscription data from Chargify. Your Pydantic models act as a second line of defense, rejecting any response that contains data not explicitly defined in your model, which helps prevent accidental data exposure.

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