How to Use the Kinde (Modern Authentication) MCP in Pydantic AI

Q: How do I use the Kinde (Modern Authentication) tools with Pydantic AI?

Install pydantic-ai-slimmcp and define an MCPToolset with your Vinkius server URL. Pass that toolset to your Agent's toolsets parameter. It's designed to be straightforward.

Q: What happens if the Kinde MCP server returns data that doesn't match my Pydantic model?

Pydantic AI will raise a ValidationError at runtime. This is its main feature—it protects your application from processing unexpected or corrupt data from an API.

Q: Can I use Pydantic AI to manage permissions for Kinde (Modern Authentication) users?

Yes. You'd have your agent call listpermissions and listroles. The returned lists are validated, so you can trust the data before using it to decide how to call updateuser for a specific person.

Manage Kinde with Pydantic AI for type-safe, validated identity operations. If the data's wrong, your agent will tell you.

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Connect Kinde (Modern Authentication) MCP to Pydantic AI

Create your Vinkius account to connect Kinde (Modern Authentication) 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 Kinde (Modern Authentication) with Pydantic AI

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Update Kinde Users Without Silent Failures

When your agent calls `update_user` to change a user's role, Pydantic AI validates the server's response against your Pydantic model. You get exactly the data structure you expect, every time. If the Kinde MCP Server ever returns a malformed object or an unexpected field, your code doesn't just fail silently. It raises a `ValidationError` immediately. You know instantly when something is wrong.

Build Correct-by-Construction Identity Tools

This framework forces correctness from the start. Before your agent can even call `create_user`, you define the input as a Pydantic model. This structure prevents the agent from hallucinating parameters or sending badly formatted data to Kinde. Think of it as a contract. Your agent agrees to provide a `given_name` and `family_name`, and the tool agrees to use them. If the contract is broken on either side, the operation stops.

Model-Agnostic Org Management with this MCP Server

Use whatever LLM you prefer—OpenAI, Anthropic, a local model, it doesn't matter. Pydantic AI sits between your model and the Kinde MCP tools, handling the structured data conversion. Your agent's only job is to decide *when* to call `create_organization` or `list_organizations`. Pydantic AI takes care of the how, ensuring the data flowing between your agent and Kinde is always clean and predictable.

Setup guide

Set up Kinde (Modern Authentication) 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": {
        "kinde-modern-authentication-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Kinde (Modern Authentication) transactions")
print(result.output)

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Common questions about Kinde (Modern Authentication) MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]` and define an `MCPToolset` with your Vinkius server URL. Pass that toolset to your `Agent`'s `toolsets` parameter. It's designed to be straightforward.

Pydantic AI will raise a `ValidationError` at runtime. This is its main feature—it protects your application from processing unexpected or corrupt data from an API.

Yes. You'd have your agent call `list_permissions` and `list_roles`. The returned lists are validated, so you can trust the data before using it to decide how to call `update_user` for a specific person.

It does. Pydantic AI and the underlying MCP client support async operations. You can `await` tool calls for `create_user` or `list_users` without blocking your application.

It processes Kinde user profiles, organization lists, and role definitions. The security benefit of Pydantic AI is runtime data validation. It acts as a security gate, ensuring that malformed data from the API never reaches your application logic, which prevents a whole class of potential bugs and vulnerabilities.

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