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 LangChain 10 tools — connect in under 2 minutes

Name: Kontent.ai
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Kontent.ai through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "kontentai": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Kontent.ai, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* 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.

LangChain's ecosystem of 500+ components combines seamlessly with Kontent.ai through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

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

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Kontent.ai via MCP

Why Use LangChain with the Kontent.ai MCP Server

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

The largest ecosystem of integrations, chains, and agents. combine Kontent.ai MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Kontent.ai queries for multi-turn workflows

Kontent.ai + LangChain Use Cases

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

RAG with live data: combine Kontent.ai tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Kontent.ai, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Kontent.ai tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Kontent.ai tool call, measure latency, and optimize your agent's performance

Kontent.ai MCP Tools for LangChain (10)

These 10 tools become available when you connect Kontent.ai to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Kontent.ai + LangChain FAQ

Common questions about integrating Kontent.ai MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.