How to Use the Kontent.ai MCP in Pydantic AI

Q: How do I initialize Kontent.ai with Pydantic AI?

Install pydantic-ai-slimmcp. Create an MCPToolset pointing to your Vinkius HTTP endpoint and pass it to the Agent via the toolsets parameter.

Q: Does Pydantic AI support Kontent.ai search?

Yes. The searchkontentai tool accepts standard query parameters. You define the input model, and Pydantic AI ensures the agent formats the query correctly before execution.

Type-safe Kontent.ai integration for Pydantic AI. Validate every content item and schema at runtime.

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Connect Kontent.ai MCP to Pydantic AI

Create your Vinkius account to connect Kontent.ai 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 Kontent.ai with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Strict Content Retrieval

`get_content_item` fetches a specific article by codename and hands it to Pydantic AI for immediate validation. If the CMS payload misses a required field, your agent throws a validation error instead of hallucinating data. `list_content_items` pulls batches of articles. You define the exact Pydantic model for the expected response, ensuring your downstream Python code never crashes due to unexpected JSON structures.

Kontent.ai MCP Server Schema Validation

`list_content_types` queries your project's architecture. Your agent cross-references these schemas against your internal code models to guarantee alignment before processing any actual content. `get_content_type` drills down into the specific schema details. Pydantic AI strictly enforces the returned types, so a text field is always treated as text and never accidentally cast as an integer.

Taxonomy and Asset Queries

`list_content_assets` retrieves the media library metadata. Your agent validates the image URLs and alt text formats before passing them to the front-end application. `list_taxonomies` fetches your classification tags. Because Pydantic AI enforces structure, you can trust that the agent will only use valid taxonomy codenames in subsequent API calls.

Setup guide

Set up Kontent.ai 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": {
        "kontentai-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Kontent.ai MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`. Create an `MCPToolset` pointing to your Vinkius HTTP endpoint and pass it to the `Agent` via the `toolsets` parameter.

Yes. The `search_kontent_ai` tool accepts standard query parameters. You define the input model, and Pydantic AI ensures the agent formats the query correctly before execution.

The framework fails loudly. If the API response doesn't match your Pydantic model, it throws a validation error immediately. No silent failures.

Yes. Pydantic AI is model-agnostic. You can connect an Ollama model to the MCP server and it will execute tools exactly like OpenAI or Anthropic would.

Every request runs in an isolated, stateless environment. Your taxonomy structures and asset metadata are transmitted via an encrypted tunnel and the execution context is destroyed the second the response completes.

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