How to Use the Churnkey MCP in Pydantic AI

Get type-safe customer retention data in Pydantic AI by validating every Churnkey API response at runtime.

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Connect Churnkey MCP to Pydantic AI

Create your Vinkius account to connect Churnkey 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 Churnkey with Pydantic AI

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Type-Safe Updates via Churnkey MCP Server

`update_billing_contacts` and `bulk_update_billing_contacts` modify billing information with strict schema validation. Your agent cannot execute updates with malformed email addresses or broken phone numbers. The framework checks the API response against Pydantic models before returning data to your application. If Churnkey returns an unexpected schema, the system raises a validation error immediately.

Strict GDPR Data Management

`get_customer_gdpr_data` and `delete_customer_gdpr_data` guarantee that customer privacy records match your internal types. Your agent reads the compliance payload and validates every field before processing deletions. You avoid database corruption by rejecting bad payloads at the boundary. This structure keeps your GDPR pipelines predictable and safe.

Validate Retention Session Records

`get_retention_session_details` and `list_customer_retention_history` pull cancellation logs into your structured Python models. Your agent parses exit surveys and feedback fields without risk of runtime crashes from missing keys. This validation ensures your downstream analytics engines receive clean data. You write less defensive code because the types are guaranteed.

Setup guide

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

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

result = await agent.run("List recent Churnkey transactions")
print(result.output)

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Common questions about Churnkey MCP in Pydantic AI

Install the MCP extra for Pydantic AI. Use the unified toolset class pointing to your Vinkius HTTP URL, then pass that toolset directly to your agent.

The framework fails loudly and raises a validation error. This prevents your agent from making decisions based on malformed or incomplete customer data.

Yes. Pydantic AI is model-agnostic, so you can pair these retention tools with OpenAI, Anthropic, or local models.

No, the old HTTP client is deprecated. You should use the unified toolset class for all HTTP and SSE transport connections to the hosted MCP server.

Vinkius processes all GDPR data requests in ephemeral V8 isolates that destroy themselves after execution. No customer records are written to disk or stored in persistent logs during transit.

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