How to Use the Kentico (CMS & DXP) MCP in Pydantic AI

Q: How do I initialize the Kentico (CMS & DXP) toolset in Pydantic AI?

Install pydantic-ai-slimmcp and use the unified MCPToolset class pointing to your Vinkius HTTP endpoint. Pass this toolset into the toolsets list of your Agent.

Build type-safe Kentico CMS integrations using Pydantic AI to validate every document and system object schema at runtime.

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Connect Kentico (CMS & DXP) MCP to Pydantic AI

Create your Vinkius account to connect Kentico (CMS & DXP) 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 Kentico (CMS & DXP) with Pydantic AI

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Runtime type validation for Kentico data

The `get_site_document` tool ensures that every document returned by Kentico matches your expected Pydantic AI models before your code processes it. This MCP Server ensures that every document returned matches your expected Pydantic AI models before your code processes it. Pydantic AI validates the JSON payloads at runtime. If the Kentico CMS returns unexpected fields or missing data, the SDK raises a validation error immediately rather than letting bad data corrupt your database.

Type-safe system object management in Pydantic AI

The `update_system_object` tool demands strict validation to prevent system errors within a typed MCP framework. By using this alongside `create_system_object`, you guarantee that only correctly formatted configurations are sent to Kentico. You set this up by passing the `MCPToolset` directly into your Pydantic AI Agent constructor. The agent automatically maps the tool schemas, ensuring inputs like user details for `get_user` conform to strict types.

Audit custom tables without silent failures

The `list_custom_table_rows` tool prevents silent schema changes from breaking downstream applications when fetching large datasets using this MCP integration. If a table structure changes in Kentico, your Python code catches it instantly. This makes Pydantic AI ideal for running data migrations or automated audits where data integrity is your top priority.

Setup guide

Set up Kentico (CMS & DXP) 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": {
        "kentico-cms-dxp-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Kentico (CMS & DXP) transactions")
print(result.output)

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Common questions about Kentico (CMS & DXP) MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]` and use the unified MCPToolset class pointing to your Vinkius HTTP endpoint. Pass this toolset into the `toolsets` list of your `Agent`.

The framework will raise a validation error at runtime. This prevents your agent from processing malformed documents or incorrect user records retrieved via `get_user`.

Yes. You can connect using either Streamable HTTP or Server-Sent Events (SSE) transports, allowing real-time MCP tool execution and reliable streaming of CMS updates.

Yes. Pydantic AI is model-agnostic, meaning you can run your Kentico automation workflows using Anthropic, Gemini, or even local models while maintaining full type safety.

All authentication is handled at the Vinkius gateway level using a single secure token. Your Python runtime never handles raw Kentico admin credentials when calling `list_users` or updating documents.

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