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 CrewAI 10 tools — connect in under 2 minutes

Name: Kontent.ai
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Kontent.ai through Vinkius, pass the Edge URL in the `mcps` parameter and every Kontent.ai tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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

from crewai import Agent, Task, Crew

agent = Agent(
    role="Kontent.ai Specialist",
    goal="Help users interact with Kontent.ai effectively",
    backstory=(
        "You are an expert at leveraging Kontent.ai tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Kontent.ai "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, Kontent.ai becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Kontent.ai tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

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

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from Kontent.ai

Why Use CrewAI with the Kontent.ai MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Kontent.ai through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Kontent.ai + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Kontent.ai for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Kontent.ai, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Kontent.ai tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Kontent.ai against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Kontent.ai MCP Tools for CrewAI (10)

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

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

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Kontent.ai + CrewAI FAQ

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

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.