Kontent.ai MCP. Audit content types and query structured data from anywhere.
Kontent.ai MCP connects your AI agent directly to Kontent.ai's Delivery API, giving you immediate access to structured content data. You can analyze entire content models, list every available content type and schema, query specific taxonomy groups, find assets in the library, or perform deep searches across your modular repository. Stop guessing what your content looks like; get a complete overview of your project's data structure from any compatible client.
Give Claude and any AI agent real-world access
Examine the full content types and schemas that define what kind of data exists within your repository.
Locate specific images, files, or other media stored in the centralized content library.
Understand how your content is categorized by querying taxonomy groups and associated terms.
Pull the full, modular details for any single article, product page, or piece of content using its unique codename.
Run filtered searches across your entire collection to find relevant items based on specific query parameters.
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What AI agents can do with Kontent.ai: 10 Tools for Content Modeling
These tools let you systematically audit your entire content repository structure, from listing every available item to deep-diving into specific data schemas and asset libraries.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Kontent.ai MCPList Content Items
Lists all available content item codenames, helping you find the unique identifier for any article or page.
Get Content Item
Retrieves the full set of modular data fields and content for a specific item when...
List Content Types
Shows every defined content schema, allowing you to audit what types of content your...
Get Content Type
Provides detailed metadata about a single content type, showing all its required...
List Taxonomies
Lists every taxonomy group defined in your project's structure.
Get Taxonomy Group
Retrieves specific details about a single content classification group, including its terms and hierarchy.
List Project Languages
Lists all languages that are supported for content within your Kontent.ai project.
List Content Assets
Queries and lists every media asset, like images or files, stored in the library.
Search Kontent Ai
Performs a flexible search across your entire content delivery repository using...
Get Content Type Element
Gets specific metadata for an element within a type, useful when you need to know...
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Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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The struggle to understand a CMS without developer access.
Today, if you need to know what fields are available on the 'Article' type—say, whether or not an author field is mandatory—you have to log into the Content Management System. You click through tabs, navigate schema views, and copy-paste names just to confirm a data point for a meeting. It's slow, it's prone to human error, and you can't automate the discovery process.
With this MCP, your agent handles that friction instantly. Instead of manual clicks, you prompt: 'Audit all content types.' The system immediately returns a clean, structured list of every schema and its fields. You get the data architecture overview in seconds.
Structured Content Discovery with Kontent.ai MCP
The biggest time sink is verifying content existence or structure. Checking if a product page codename exists, or confirming the required fields for a new article type, requires multiple manual steps across different backend tools.
Now, you can ask your agent to perform these checks in one go. By using `list_content_items` and then cross-referencing that list with schema details from `get_content_type`, you gain immediate data certainty. The entire process becomes a single conversation.
What Kontent.ai MCP does for your AI
Need to understand how complex structured content works? This MCP lets you connect your AI agent directly to Kontent.ai’s Delivery API, giving you full visibility into your modular content without ever touching the UI. Instead of sifting through dashboards or guessing what fields exist, you can ask your agent to audit your entire data model, listing all available schemas and their specific elements.
If you're building a site that needs structured, reliable content—think product pages with multiple variants, or complex articles requiring author attribution and date ranges—this is the connector for you. You can pull down all necessary metadata, from finding codenames using list_content_items to querying entire taxonomy groups. All of this powerful access lives within Vinkius, making it easy to plug Kontent.ai data into your agent workflow, regardless of whether you're working in Cursor or Claude.
019d75c2-f15e-737f-b478-5c7a7495bc61 How to set up Kontent.ai MCP
The bottom line is that this MCP turns complex, siloed API calls into simple natural language instructions for your agent.
Subscribe to the MCP and provide your Project ID from Kontent.ai's settings.
If secure access is needed, input your Delivery API Key into the configuration panel.
Tell your agent what you need—for example, 'Audit all content types for me,' or 'Find assets related to Q4 promotions.' The MCP executes the query and returns structured data.
Who uses Kontent.ai MCP
This is for technical content architects or developers who are tired of manually inspecting CMS dashboards just to figure out a data field name. If you're building an application that needs structured, governed content, this MCP cuts the guesswork.
Uses this MCP to map out and audit all existing content types and taxonomies before a new feature build starts.
Calls get_content_item or runs search_kontent_ai to fetch specific, formatted data needed for dynamic rendering in an application.
Uses the MCP to verify if a required piece of content exists by listing items and checking its associated taxonomy groups.
Benefits of connecting Kontent.ai MCP
Stop logging into the Kontent UI just to check a schema. Use list_content_types or get_content_type to programmatically audit your entire content model with a single prompt.
Need to find an image? Instead of navigating through file folders, use list_content_assets to query and locate specific media files directly from the MCP.
Don't rely on memory for unique identifiers. Use list_content_items first to gather all necessary codenames before you attempt to pull down content using get_content_item.
When building a site that relies heavily on classification, use the MCP to list taxonomy groups and map relationships across your data via get_taxonomy_group.
Forget running simple keyword searches. Use search_kontent_ai to filter content using multiple parameters, dramatically improving search accuracy for agents.
Kontent.ai MCP use cases
The product page needs a new field.
A developer realizes the Product Catalog type is missing a 'Warranty Info' field. Instead of emailing content ops, they ask their agent to run list_content_types and inspect the schema using get_content_type, immediately identifying the gap for the product owner.
I need all articles mentioning a specific campaign.
A marketing specialist needs to pull every article related to 'Summer Sale.' They prompt their agent with a search query, which uses search_kontent_ai and returns a list of relevant codenames. The agent then fetches the full content for review.
How do I find the right image?
A designer needs an asset from last year's campaign but can’t remember the file name. They use list_content_assets and filter by date range, getting a list of potential images they can then pass to their agent for retrieval.
Mapping out content dependencies.
A data architect needs to know if the 'Article' type depends on a specific taxonomy group. They first use list_taxonomies and then drill down with get_taxonomy_group to map the exact relationships for their system.
Kontent.ai MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Treating it like a simple database query
Asking the agent, 'What are all my articles?' and expecting a list of every article's full text. This only retrieves metadata.
First, use list_content_items to get codenames. Then, pass one or more codenames to get_content_item to retrieve the actual modular content for those specific items.
Assuming all schemas are visible
Trying to query a field that doesn't exist on a given content type, causing an error.
Always start by running list_content_types and then use get_content_type on the specific schema name. This confirms which fields are actually available before you try to read them.
Over-relying on general keywords
Using a broad search query that returns thousands of irrelevant results because it lacks context or filters.
Use search_kontent_ai and pass specific parameters, like filtering by a known taxonomy group ID (obtained via get_taxonomy_group) to narrow the results.
When to use Kontent.ai MCP
Use this MCP if your core problem is structured data governance or content discovery. If you need to build an application that reads and understands complex, modular schemas (like product variants with multiple required attributes), this connector gives you the necessary visibility. You are modeling the data structure, not just generating text.
Do NOT use this MCP if your goal is simple natural language generation or basic web scraping (e.g., 'summarize this webpage'). For those tasks, general purpose text models suffice. If you only need to know what languages are supported, list_project_languages works, but if you need the actual content, you must use the full item retrieval tools like get_content_item. This MCP is for data engineers and architects; it's about structure first.
Frequently asked questions about Kontent.ai MCP
How do I check what content types are available using the Kontent.ai MCP? +
Run the list_content_types tool first. This will give you a complete list of every schema defined in your project, allowing you to see all possible content structures.
What is the difference between `get_content_item` and `search_kontent_ai`? +
list_content_items helps you find a specific item's unique codename. If you have that codename, use get_content_item to pull all its data. Use search_kontent_ai when you need to find multiple items based on keywords or filters.
Can I list the taxonomy groups using Kontent.ai MCP? +
Yes, use the list_taxonomies tool. This gives you an overview of all classification systems in your project, which helps map content relationships.
Does this MCP help me find images or files? +
You can find assets using list_content_assets. It queries the content library and returns a list of file metadata, so you know exactly what media is available.
What if I want to see the fields for 'product' type? +
You need to use get_content_type. If you provide the content type name, this tool returns all its specific elements and their options, letting you audit the schema.