Kontent.ai MCP. Analyze your content model and assets from your agent.
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Kontent.ai MCP Server gives your agent direct access to Kontent.ai's headless content API. You can list all content items, audit content types (schemas), and query taxonomies to understand your entire content model.
Use the `search_kontent_ai` tool to find specific articles or assets, or `get_content_type` to see what fields a certain content type uses.
This is how you programmatically map and manage modular content across your entire digital ecosystem.
What your AI agents can do
Get content item
Retrieves all modular content for a single item when you provide its codename.
Get content type
Fetches the full schema and metadata for a specified content type.
Get content type element
Gets metadata and options for a specific field element within a content type.
Fetch the full content details of any item when you know its codename.
List and inspect content types to build a detailed map of your data structure.
Find and analyze taxonomy groups and terms that categorize your content.
Perform targeted searches across your entire content delivery repository, including assets.
Locate all images and files stored within the content library.
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Kontent.ai MCP Server: 10 Tools for Content Operations
Use these tools to read, audit, and search across your entire modular content model and asset library.
019d75c2get content item
Retrieves all modular content for a single item when you provide its codename.
019d75c2get content type
Fetches the full schema and metadata for a specified content type.
019d75c2get content type element
Gets metadata and options for a specific field element within a content type.
019d75c2get taxonomy group
Retrieves details and structure for a specific content taxonomy group.
019d75c2list content assets
Queries and lists all digital assets, like images and files, stored in the content library.
019d75c2list content items
Lists the codenames of all content items, useful for finding articles or pages.
019d75c2list content types
Lists every available content type (schema) defined in your project.
019d75c2list project languages
Lists all languages supported by your content project.
019d75c2list taxonomies
Lists all major taxonomy groups available for content categorization.
019d75c2search kontent ai
Searches across your entire content repository using specific query parameters.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
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Make Your AI Do More
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- Use this MCP plus 4,700+ others, all in one place
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- Works with Claude, ChatGPT, Cursor, and more
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What you can do with this MCP connector
You connect your agent to the Kontent.ai Delivery API using this server. Your agent can look at and manage all your modular content directly. It'll let you list every content item, check your content types (schemas), and query your taxonomies to get a full picture of your content model. You'll use this to programmatically map and manage modular content across your whole digital setup.
To pull specific content records, your agent uses get_content_item when you give it a codename. To map the content schema, you can list all content types with list_content_types, and then inspect a specific type using get_content_type. You can also dig into what fields a content type uses by calling get_content_type_element.
To query content categorization, your agent first lists all major taxonomy groups with list_taxonomies, then gets details for a group using get_taxonomy_group. To find all images and files in your content library, your agent calls list_content_assets. You can search across your entire content repository using search_kontent_ai with specific search parameters.
To find all content item codenames, your agent uses list_content_items. You can also check what languages your content project supports with list_project_languages.
How Kontent.ai MCP Works
- 1 Subscribe to the Kontent.ai MCP Server and provide your Project ID and API Key to your agent.
- 2 Your agent uses a natural language prompt (e.g., 'Find all articles about winter sales').
- 3 The server maps the prompt to the correct tool (e.g.,
search_kontent_ai), executes the API call, and returns the structured content data to your agent.
The bottom line is, your agent acts as the translator, taking your intent and turning it into precise API calls to Kontent.ai.
Who Is Kontent.ai MCP For?
The technical content strategist who needs to understand the structure of a massive content repository. The data architect who needs to verify if content schemas are stable before a migration. The marketing operations engineer who needs to programmatically search across thousands of published assets. If your job involves building tools on top of existing content, this is for you.
Uses list_content_types and get_content_type to validate the existing data model and identify missing fields before content ingestion.
Uses get_content_item to pull the raw, final content of a specific article for compliance checks or integration testing.
Uses list_content_assets and list_content_items to audit the content library's structure and find unique asset identifiers for deployment.
Uses search_kontent_ai to build automated reports that cross-reference specific keywords with content types and taxonomy groups.
What Changes When You Connect
- Understand your entire data structure instantly. Instead of guessing, use
list_content_typesto see every schema available in your project. - Find specific content records without manual searching. Pass a codename to
get_content_itemand get the full, modular content immediately. - Build precise reports by cross-referencing data. Use
search_kontent_aito query content across multiple criteria (e.g., 'Article' + 'Winter Sale'). - Audit your content taxonomy. Use
list_taxonomiesandget_taxonomy_groupto map out how your content is categorized and grouped. - Manage assets programmatically.
list_content_assetslets you find and list images and files without touching the UI. - Save time auditing.
list_content_itemsquickly provides a list of codenames, so you know exactly which pieces of content exist before you try to fetch them.
Real-World Use Cases
Auditing a new content schema
A content architect needs to know if a new 'Product' type has the necessary fields. They ask their agent to use list_content_types first, then run get_content_type on 'Product' to confirm the required fields are present. This validates the data model before development begins.
Finding content for a live campaign
The marketing team needs all banners related to 'Q3 Promotion'. They instruct their agent to use search_kontent_ai with the query 'Q3 Promotion'. The agent returns a list of relevant codenames, which can then be passed to get_content_item to pull the full, ready-to-use content.
Mapping asset dependencies
A developer needs to know every image used on the homepage. They ask the agent to run list_content_assets, which returns a list of all stored assets. They can then pass these asset IDs to other tools to track which content items use them.
Verifying content relationships
A technical writer wants to know the possible categories for 'Author' content. They ask the agent to use list_taxonomies and then get_taxonomy_group to see the group structure, ensuring all content is placed correctly.
The Tradeoffs
Treating content like simple database rows
A developer tries to treat content as a simple database table and just runs a general 'select * from articles'. This misses the modular structure, asset links, and taxonomy rules.
→
Instead, use list_content_items to get the codenames, then get_content_item for the full modular payload. For categorization, always check get_taxonomy_group before assuming a field exists.
Ignoring schema details
Trying to access a field like 'author_bio' without knowing if the 'Article' content type actually defines that field. This leads to runtime errors.
→
Always check the schema first. Use list_content_types to see all available types, then get_content_type to audit the specific fields for the type you need.
Over-relying on manual UI navigation
A user manually clicks through the Kontent UI to find a specific asset ID, which is slow and prone to human error.
→
Just tell your agent to run list_content_assets. It gets the list of IDs instantly, saving the clicks and the manual data copy/paste.
When It Fits, When It Doesn't
Use this if your primary goal is inspecting, auditing, or programmatically retrieving content structures and data relationships. You need to know what content exists, how it's structured (the schema), and where its assets are stored. The get_content_type and list_taxonomies tools are your starting points for understanding the model. Use search_kontent_ai when you know the keywords but don't know the exact codename. Don't use this if you just need to write content; this server only reads. If you need to change or publish content, you'll need a separate write-enabled service.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Kontent.ai. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Sifting through content assets and schemas is a nightmare.
Today, figuring out what content exists means jumping between the content management UI, the asset library, and the schema documentation. You copy a codename from one place, verify it in another, and then you have to piece together the related assets and taxonomy rules manually. It's slow and you always miss something.
With this MCP server, you just tell your agent to look. It uses `list_content_types` to show you the data model and `list_content_assets` to show you the files. You get the entire, structured overview in one go, without ever leaving your agent interface.
Kontent.ai MCP Server: Get Content Items
Before, fetching a specific article meant navigating deep into the CMS, finding the right ID, and then hoping the content payload was complete and usable. If you missed one step, the integration failed.
Now, your agent runs `get_content_item` and pulls the full, modular content payload directly. You get a clean, machine-readable object that's ready for immediate use.
Common Questions About Kontent.ai MCP
How do I find the codename for a specific article using `list_content_items`? +
You run list_content_items first. This gives you a list of all available codenames (e.g., 'Homepage', 'Article-Q3'). Then, you pass the specific codename to get_content_item to retrieve the article's full content.
What is the difference between `get_content_type` and `list_content_types`? +
list_content_types shows you all schemas (e.g., 'Article', 'Page'). get_content_type lets you drill down and see the exact fields and rules for one specific schema.
Can I search for content using `search_kontent_ai`? +
Yes. You use search_kontent_ai by providing query parameters. This tool performs a filtered search across your entire content repository, finding items that match your criteria.
Does `list_content_assets` show metadata for images? +
Yes. list_content_assets queries your content library and returns metadata for stored images and files, letting you audit the asset library's contents.
What do I need to use the `get_content_type_element` tool? +
You need the content type ID and the element's specific ID. This tool fetches metadata for a single element within a defined content type, giving you details like field type or required options.
How do I list all available content types using `list_content_types`? +
You call list_content_types with no parameters. It returns a comprehensive list of all available schemas (content types) defined in your Kontent.ai project.
If I need to find taxonomy details, which tool should I use—`list_taxonomies` or `get_taxonomy_group`? +
list_taxonomies shows you all the available taxonomy groups. Then, you use get_taxonomy_group to pull specific details for one of those groups.
Can `get_content_item` handle multiple codenames in one request? +
No, get_content_item requires a single codename per call. You must make a separate API request for every content item you want to inspect.
Where do I find my Project ID? +
Log in to Kontent.ai, go to Project Settings > API keys, and copy the Project ID.
What is a codename? +
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.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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