Kontent.ai
MCP ServerFree** - Create, manage, and explore your content and content model using natural language in any MCP-compatible AI tool.
Capabilities9 decomposed
natural language content model introspection and exploration
Medium confidenceExposes Kontent.ai's content model schema (content types, elements, taxonomies, workflows) through MCP tools that parse natural language queries and translate them into API calls to the Kontent.ai Management API. The MCP server acts as a semantic bridge, allowing users to ask questions like 'show me all content types with a rich text field' without needing to understand REST API structure or JSON schema syntax.
Bridges natural language queries directly to Kontent.ai's Management API schema without requiring users to understand REST endpoints or JSON structure; implements semantic routing of conversational queries to specific API calls for content type, element, and taxonomy discovery.
Provides conversational access to content model metadata that would otherwise require manual API exploration or dashboard navigation, making schema discovery accessible to non-technical users in any MCP-compatible AI tool.
content item creation and mutation via natural language
Medium confidenceTranslates natural language descriptions of content into structured API calls that create or update content items in Kontent.ai. The MCP server parses user intent (e.g., 'create a blog post about AI with title and body'), maps fields to the appropriate content type schema, validates against content model constraints, and executes the Management API request. Supports field-level validation and error reporting.
Implements a semantic layer that maps free-form natural language descriptions to Kontent.ai's strongly-typed content model, performing field validation and type coercion before API submission. Uses MCP's tool schema to expose content type definitions dynamically.
Enables content creation through conversational AI without requiring users to navigate the Kontent.ai UI or write API code, making content generation accessible to non-technical team members within their existing AI tool.
content item retrieval and filtering with natural language queries
Medium confidenceTranslates natural language search and filter requests into Kontent.ai's Content Delivery API queries, supporting filters by content type, taxonomy, status, date ranges, and custom metadata. The MCP server parses intent from queries like 'show me all published blog posts from the last month' and constructs the appropriate API request with proper filter syntax and pagination.
Implements a natural language to Kontent.ai query translator that handles content type filtering, taxonomy-based faceting, and date range queries. Uses MCP tool definitions to expose available filters dynamically based on project schema.
Provides conversational content discovery without requiring knowledge of Kontent.ai's filter syntax or API structure, making content retrieval accessible to non-technical users while maintaining full query expressiveness.
content workflow state transitions via natural language commands
Medium confidenceExposes Kontent.ai's workflow state machine through MCP tools that allow users to transition content items between workflow states (draft, scheduled, published, archived) using natural language commands. The server validates state transitions against the project's workflow configuration and executes the Management API calls to update item status.
Maps natural language workflow commands to Kontent.ai's state machine, validating transitions against project-specific workflow rules before executing API calls. Exposes available states and valid transitions dynamically based on project configuration.
Enables content lifecycle management through conversational commands without requiring users to navigate the Kontent.ai UI or understand workflow state syntax, making content governance accessible within AI tools.
mcp tool schema generation from kontent.ai project metadata
Medium confidenceDynamically generates MCP tool definitions by introspecting the Kontent.ai project's content model, exposing content types, elements, taxonomies, and workflows as callable tools with proper JSON schemas. This enables the MCP server to adapt its capabilities to the specific project structure without hardcoding tool definitions, allowing each project to have a customized set of available operations.
Implements dynamic MCP tool generation by introspecting Kontent.ai's Management API to extract content model metadata and translating it into JSON schema-compliant tool definitions. Enables project-specific customization without hardcoding.
Allows a single MCP server implementation to support any Kontent.ai project by dynamically adapting its tool set to the project's content model, eliminating the need for project-specific server configurations or code changes.
taxonomy term management and querying
Medium confidenceProvides MCP tools for exploring and managing taxonomy terms in Kontent.ai, allowing users to query available terms, their hierarchies, and create new terms through natural language. The server translates taxonomy queries into Management API calls and handles term creation with proper hierarchy and metadata assignment.
Exposes Kontent.ai's taxonomy system through MCP tools with natural language query support, handling both flat and hierarchical taxonomies. Translates taxonomy queries into Management API calls with proper hierarchy traversal.
Enables taxonomy-based content organization and discovery through conversational AI without requiring users to navigate taxonomy management interfaces or understand API structures.
content asset management and reference handling
Medium confidenceProvides MCP tools for managing digital assets (images, documents, videos) in Kontent.ai, including uploading assets, querying asset metadata, and linking assets to content items. The server handles asset upload through the Management API, manages asset references, and supports asset filtering by type and metadata.
Implements asset management through MCP tools that handle file upload, metadata assignment, and asset-to-content linking. Abstracts Kontent.ai's asset API complexity behind natural language commands.
Enables asset management and linking within AI workflows without requiring direct API calls or file system access, making media handling accessible to non-technical users in conversational interfaces.
multi-language content variant management
Medium confidenceExposes Kontent.ai's language variant system through MCP tools, allowing users to create, update, and query content in multiple languages. The server handles language-specific content variants, manages language fallback chains, and supports querying content by language or locale.
Implements language variant management by exposing Kontent.ai's language system through MCP tools, handling language-specific content creation and querying with proper locale mapping.
Enables multilingual content management through conversational commands without requiring users to understand language variant APIs or locale-specific syntax.
content preview and preview url generation
Medium confidenceProvides MCP tools for generating preview URLs and accessing preview content for unpublished or draft items in Kontent.ai. The server constructs preview URLs with proper authentication tokens and supports preview filtering by language, workflow state, and content type.
Generates authenticated preview URLs and retrieves preview content through MCP tools, handling authentication token generation and URL construction for Kontent.ai's preview system.
Enables content review workflows within AI tools by providing preview access without requiring users to navigate the Kontent.ai UI or manage authentication tokens manually.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓Content architects and editors using AI tools to understand project structure
- ✓Non-technical stakeholders exploring content models via Claude or other MCP clients
- ✓Teams migrating from other CMS platforms who need quick content model discovery
- ✓Content creators and editors using AI assistants to draft and publish content
- ✓Developers building AI-powered content generation pipelines
- ✓Teams automating content creation workflows through MCP agents
- ✓Content editors and marketers querying content via AI assistants
- ✓AI agents that need to retrieve and process content as part of larger workflows
Known Limitations
- ⚠Read-only exploration — cannot modify content model structure through natural language
- ⚠Query complexity limited by LLM's ability to parse nested schema responses
- ⚠No caching of schema — each query triggers a fresh API call to Kontent.ai
- ⚠Requires explicit content type specification — cannot auto-detect from description alone
- ⚠Field validation errors are returned as-is from Kontent.ai API; error messages may be opaque
- ⚠No support for complex nested content structures (linked items, components) in initial versions
Requirements
Input / Output
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** - Create, manage, and explore your content and content model using natural language in any MCP-compatible AI tool.
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