# AppFollow MCP for AI Agents MCP > AppFollow gives your AI client deep visibility into app store performance. Track star ratings, monitor daily ranking changes across global charts, and analyze thousands of user reviews instantly to understand public sentiment. It’s built for rapid app reputation management. ## Overview - **Category:** customer-support - **Price:** Free - **Tags:** app-reviews, sentiment-analysis, aso, customer-feedback, reputation-management, store-rankings ## Description Running an app means constantly managing its reputation. AppFollow brings powerful insights from major app stores directly to your AI agent. Instead of manually checking multiple sites or spending hours sifting through language barriers, you can ask your AI client to analyze all user feedback and performance metrics at once. You'll get instant summaries on overall sentiment, see exactly why rankings dipped last week, and compare how your app performs against competitors globally. Connecting AppFollow via Vinkius lets any compatible AI client—like Cursor or Claude—access this intelligence in natural conversation, turning raw review data into actionable product insights for PMs and marketing teams alike. ## Tools ### get_account_check Verifies that your AppFollow account is properly connected for usage. ### get_app_info Retrieves basic, foundational details about a specific app from the store. ### get_rankings Tracks your app's current position in various relevant store charts. ### get_ratings_history Provides historical data on star ratings, showing trends over a selected time period. ### get_ratings Gathers the current distribution of star ratings for your app (e.g., how many 5-star vs. 3-star reviews exist). ### get_reviews_ai_summary Generates a concise, AI-powered summary of the most recent user feedback. ### get_reviews_summary Provides an overall synthesis of reviews, including average rating and key themes. ### list_reviews Lists individual app reviews for a specific product in any major store market. ## Prompt Examples **Prompt:** ``` What are the main complaints about my app since last month? ``` **Response:** ``` **App Review Summary: Last 30 Days** Based on over 950 reviews, the overall sentiment is positive (4.1/5 stars), but three major themes emerged: * 🐛 **Performance:** Frequent reports of slow loading time after updates. * 🎨 **UI/UX:** Multiple requests for a dark mode option and better widget customization. * ⚙️ **Features:** Users are asking to integrate with calendar services directly. **Action:** Prioritize addressing the performance issues first. ``` **Prompt:** ``` How did my ranking change in 'Productivity' last week? ``` **Response:** ``` **Ranking Trend: Productivity Category (US)** The app started at #12 on Monday and ended at #9 today. This is an increase of 3 spots. * **Monday:** #12 * **Tuesday:** #11 (+1) * **Wednesday:** #10 (+1) * **Thursday:** #9 (+1) This upward trend suggests recent marketing efforts are paying off. ``` **Prompt:** ``` Give me a summary of 1-star reviews for the Canadian market. ``` **Response:** ``` **Canada Review Digest (1-Star Reviews)** Reviewers consistently point to two issues: 1. **Localization:** The app fails to correctly display localized currency symbols in settings. 2. **Offline Mode:** It completely loses synchronization when the internet drops, making it unusable while traveling. ``` ## Capabilities ### Analyze User Feedback Sentiment Generates summarized insights from user reviews, identifying common complaints, praised features, and overall emotional tone. ### Track Store Rankings History Monitors your app's performance in store charts, detailing daily changes in visibility within specific categories or countries. ### Retrieve App Metadata and Info Gathers foundational data about an app, including its official name and market details across various platforms. ### Review Specific Star Rating Distributions Accesses the current star rating breakdown of your app to understand where users are leaving feedback (e.g., 1-star vs. 5-star). ### Compare App Performance Against Rivals Compares key metrics, such as ratings and rankings, between your application and specified competing titles. ## Use Cases ### Investigating a sudden drop in visibility A growth marketer notices their app dropped from #3 to #15 in the 'Productivity' category last week. They prompt the agent using `get_rankings` and then use `get_reviews_ai_summary` to check if the dip correlates with a sudden influx of negative reviews after a recent update. ### Prioritizing product features A Product Manager needs input for the next sprint. They ask their agent to analyze all user feedback using `get_reviews_ai_summary` and group complaints into top three themes, allowing them to prioritize development efforts efficiently. ### Launching in a new international market A team expanding globally wants to know the initial reception. They use `list_reviews` for multiple countries and then run `get_ratings` to gauge immediate star rating health before committing resources. ### Benchmarking against a competitor's success A marketing team wants to know why a rival app is succeeding. They use `get_app_info` and then run a comparative analysis with the agent, using data from both apps to identify best practices. ## Benefits - Stop guessing about user pain points. The `get_reviews_ai_summary` tool gives you instant, digestible insights into what users are actually complaining or praising. - Measure your ASO efforts accurately. Use the `get_rankings` tool to track daily performance changes and prove which keywords move the needle. - Understand reputation over time. The `get_ratings_history` tool maps out rating trends, letting you see if past fixes actually improved user satisfaction. - Save hours on competitive analysis. You can compare your app's performance directly against rivals using metrics gathered from `get_reviews_summary`. - Quickly assess the market. The `get_app_info` tool retrieves core metadata, giving you a baseline understanding of any competitor or target app. - Pinpoint specific bugs. By listing individual reviews with `list_reviews`, your AI agent can quickly identify recurring feature requests or critical bugs mentioned by users. ## How It Works The bottom line is you get structured app store intelligence delivered through conversational prompts, saving hours of manual research. 1. You tell your AI client what you need to know about the app—for example, 'Give me a summary of recent 1-star reviews.' 2. Your agent uses this MCP's tools to query AppFollow's database for the specific review data and ranking history. 3. The results return structured data that your AI client processes into natural language summaries, trend reports, or actionable lists. ## Frequently Asked Questions **How does AppFollow help me understand what users really think about my app?** AppFollow analyzes user feedback and gives you summarized insights on sentiment. Instead of reading hundreds of reviews, your AI agent tells you the top issues—like 'slow loading' or 'missing dark mode'—and how many times they were mentioned. **Can I use AppFollow MCP to track my app’s performance against competitors?** Yes, it lets you compare your app directly with rivals. You can pull data on ratings and rankings for multiple apps simultaneously, helping you figure out where your competitive edge is. **What kind of rating history data does AppFollow provide?** It gives you historical star rating trends over time. This means you can see if a feature fix or marketing push actually caused a measurable, positive shift in overall user satisfaction. **Is the AppFollow MCP only for US-based app stores?** No, it covers major global app stores and multiple countries. You can list and summarize reviews from different languages and regional markets to get a worldwide view of your reputation. **How do I use AppFollow MCP to find out why my rankings dropped?** You can track the rank changes using `get_rankings` and then cross-reference that date range with the reviews. The AI agent will help correlate the drop in visibility with spikes in negative user feedback.