Feature Requests

The 'used in' column in the file list within the Files Manager tells you if the file is being used in articles or not, but you cannot click it to see which ones (and open the articles). You need to click on the file first and it says 'Embeded in' (note the typo) with a dropdown that you need to click and from there you can access the articles. It would be easier if you could just click on the 'used in' pill to access this info in a popup or something, or hover over it.

Example: Our default text size is 14px for all main body text, we always have a solid 1px border around images. Being able to manually configure such settings as a standard would be great as currently we have to readjust/apply each time from the article page default.

When replacing an image in the files manager, you would need to then replace it manually in the articles, this increases the workload x10000. This tool would be much useful if it replaces the image everywhere.

Currently, when an article contains a broken link and a user clicks the article from the “Article Links” section, it simply opens the article without showing which specific link is broken. Suggested Improvement: When an article with a broken link is clicked, the system should automatically: Highlight or scroll directly to the broken link within the article editor, or display a clear indication of which link(s) are broken and where they are located in the article. Why this matters: This enhancement would save time for users managing large knowledge bases by making it much easier to locate and fix broken links without manually scanning through entire articles.

Currently, updating article URLs (slugs) requires opening each article individually in the editor, which can be time-consuming, especially when multiple links need to be changed. Suggested Improvement: Add a centralized page or management view where users can: View all article URLs/slugs in one place. Edit and update URLs directly without opening each article. Optionally sort, filter, or bulk-edit slugs for faster management. Why this matters: This enhancement would make it significantly easier to maintain consistent and SEO-friendly article URLs, especially for teams managing large knowledge bases or restructuring content.

We’re seeing 60+ “write this article” prompts for topics way outside our domain — many look bot-generated. This clogs our queue, wastes time, and risks diluting the knowledge base. What we need: Smarter controls so AI suggestions only surface for content we actually want to cover Stronger bot protection to block junk searches from triggering new-article prompts A way to exclude certain topics entirely from the “auto-suggest an article” logic Just because someone searches for something and doesn’t find it doesn’t mean we should create an article — even if it gives a short-term SEO bump. Why this matters: Keeps AI focused on suggesting high-value content without polluting KB quality and brand relevance.

When removing an image in an article and replacing it with another one, this unlinks the article from the image in the Editor Uploads ('Used In' column) and links the new one. This is great, but what would be even better is that if you deleted an image from an article that a check is run. If that article is not being used in any other articles, it could ask you if you also want to remove it from your Files Manager as well. This would keep your Files Manager up to date and not have lots of old images in there that you don't need. Or... another idea is to do this but perhaps on publishing an article it does this check and then prompts you....

Can we make the phone icon an optional feature when embedding the Chatbot to our organisation KB. Also, can we redesign this icon to be something different as this might give the chatbot users an impression that clicking on this icon will let them, contact the support team og the organisation

The new automatic creation of versions when any change is made to a previously published article is now making it difficult to identify where/how information has been updated. Can we please make the "Compare with Current Revision" button on the Revision History screen available when a different article version is selected?

Summary & Executive Brief We propose that HelpJuice develops and provides a native Model Context Protocol (MCP) server. This would allow AI agents (like those built on Claude, ChatGPT, etc.) to securely and reliably read data from a customer's HelpJuice knowledge base. In essence, this feature would transform a static KB into a dynamic, AI-ready data source, enabling customers to build powerful, context-aware AI assistants that have direct, sanctioned access to their proprietary documentation. The Problem / Opportunity The AI landscape is rapidly shifting from chat interfaces to AI agents that perform tasks autonomously. These agents need access to contextual, proprietary data to be useful. Currently, providing this data involves: Costly and Complex Integrations: Building and maintaining custom APIs and connectors for each AI platform. Unreliable Methods: Using web scraping, which is fragile and breaks with UI changes. Context Window Limitations: Manually pasting KB articles into prompts is inefficient and doesn't scale. Security Concerns: Granting broad API keys or using unsecured methods to expose data. An MCP server solves this elegantly by providing a standardized, secure, and managed bridge between the AI and the HelpJuice KB. Proposed Solution: Native HelpJuice MCP Server Develop a dedicated MCP server that exposes a controlled set of "tools" for an AI to interact with the knowledge base. The core tools would initially be focused on data retrieval. Key MCP Tools to Expose: search_knowledge_base(query: string, category?: string) Performs a semantic and/or keyword search across the entire KB or a specific category. Returns a list of relevant articles with titles, snippets, and URLs. get_article_by_id(article_id: string) Fetches the full, clean text content of a specific article by its internal ID. list_categories() Allows the AI to understand the structure of the KB by listing all top-level categories. get_articles_by_category(category_id: string) Retrieves all articles within a specific category, useful for comprehensive research. Benefits for HelpJuice Customers Build Smarter AI Assistants: Power internal help desks, customer support bots, and onboarding coaches with accurate, real-time knowledge. Drastically Reduced Development Time: Customers can connect their KB to an AI agent in hours, not weeks, using a standard protocol. Enhanced Data Security: The MCP model provides a controlled gateway. Customers never have to expose full database credentials to the AI. Improved AI Accuracy: Eliminates hallucinations by providing the AI with a direct, authoritative source of truth. Competitive Advantage: Positions HelpJuice as a forward-thinking, AI-native platform, not just a documentation tool. Benefits for HelpJuice Strategic Positioning: Becomes the "knowledge backbone" for the AI ecosystem. Increased Stickiness: Deeper integration into a customer's AI/automation stack makes the product indispensable. New Revenue Streams: This could be a premium feature or part of a higher-tier enterprise plan. Future-Proof Foundation: The MCP framework makes it easy to add more complex tools later (e.g., draft_article, suggest_edit). Technical Implementation Outline MCP Server: A standalone service (e.g., in Node.js or Python) that uses the HelpJuice API for data access. Authentication: Utilizes HelpJuice's existing API key system. The customer provides their HelpJuice subdomain and API key when configuring the MCP connection in their AI platform. Deployment: The server can be provided as: A hosted service by HelpJuice (simplest for customers). A Docker container that customers can self-host for maximum data control. Documentation: Provide clear setup guides for connecting to popular AI platforms like Claude Desktop, Cursor, and others that adopt MCP. Example User Story As a Customer Support Manager, I want our AI support agent to have instant access to our latest HelpJuice documentation, So that it can accurately answer customer questions without hallucination, reducing ticket volume and improving resolution time. Implementation Flow: User asks the AI agent: "What is our refund policy for digital products?" The AI agent uses the configured HelpJuice MCP server's search_knowledge_base tool. The MCP server returns the relevant article(s) from the live KB. The AI agent synthesizes the information and provides a concise, accurate answer to the user, citing the source. Why MCP and Not Just a Better API? While a RESTful API is powerful, MCP is specifically designed for AI agents. It's a standard that is being rapidly adopted by all major AI players. It handles state, resource management, and tool discovery in a way that is native and intuitive for AI models, reducing integration friction significantly.