Where do I find my Project ID?

Log in to Kontent.ai, go to **Project Settings** > **API keys**, and copy the Project ID.

A codename is a unique, URL-friendly identifier for content items, types, or elements (e.g., 'about_us_page').

Is the Delivery API Key mandatory?

Only if you have enabled **Secure Access** in your project settings. Otherwise, the Project ID is enough.

Kontent.ai MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Kontent.ai
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Kontent.ai through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Kontent.ai "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Kontent.ai?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Kontent.ai MCP Server

Connect your AI agent to Kontent.ai Delivery API to fetch and analyze your modular content.

Pydantic AI validates every Kontent.ai tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

Key Features

Content Item Retrieval — Fetch the full modular content of any item by its codename
Schema Auditing — List and examine content types to understand your project's data model
Taxonomy Access — Query taxonomy groups and terms for content categorization
Asset Discovery — Locate images and files stored in your content library
Smart Search — Perform filtered searches across your entire delivery repository

Simple Setup

1. Subscribe to this server
2. Get your Project ID from Kontent.ai (Project Settings > API keys)
3. (Optional) Enter your Delivery API Key if Secure Access is enabled
4. Start querying your content via natural language

The Kontent.ai MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Kontent.ai to Pydantic AI via MCP

Follow these steps to integrate the Kontent.ai MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Kontent.ai with type-safe schemas

Why Use Pydantic AI with the Kontent.ai MCP Server

Pydantic AI provides unique advantages when paired with Kontent.ai through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Kontent.ai integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Kontent.ai connection logic from agent behavior for testable, maintainable code

Kontent.ai + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Kontent.ai MCP Server delivers measurable value.

Type-safe data pipelines: query Kontent.ai with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Kontent.ai tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Kontent.ai and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Kontent.ai responses and write comprehensive agent tests

Kontent.ai MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Kontent.ai to Pydantic AI via MCP:

get_content_item

Get a specific content item by codename

get_content_type

Get details for a content type

get_content_type_element

g., options for a multiple choice element). Get metadata for a specific element in a type

get_taxonomy_group

Get details for a taxonomy group

list_content_assets

ai. Query assets from the content library

list_content_items

Use this to find codenames for specific articles, products, or pages. List all content items from Kontent.ai

list_content_types

List all content types (schemas)

list_project_languages

List supported languages

list_taxonomies

List taxonomy groups

search_kontent_ai

Search for content using query parameters

Example Prompts for Kontent.ai in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Kontent.ai immediately.

"List the last 10 content items in Kontent.ai"

"Show the schema for content type 'article'"

"Search for items related to 'Winter Sale'"

Troubleshooting Kontent.ai MCP Server with Pydantic AI

Common issues when connecting Kontent.ai to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Kontent.ai + Pydantic AI FAQ

Common questions about integrating Kontent.ai MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Kontent.ai MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.