AppFollow MCP. Track app sentiment and store chart rankings.
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AppFollow MCP Server manages app store intelligence by letting your AI client track sentiment, analyze ratings, and monitor app store rankings.
It pulls real-time data on user reviews and competitor performance across global stores. Use the AppFollow MCP Server to analyze user feedback, see rating trends, and track your app's visibility against competitors.
What your AI agents can do
Get account check
Verifies the AppFollow account connection status.
Get app info
Retrieves general metadata about a specific app from AppFollow.
Get rankings
Tracks the current store chart rankings for an app.
Verifies the credentials and connection status for the AppFollow account.
Gets basic details and metadata for a specific app from the AppFollow database.
Retrieves the current ranking position of an app in various store charts.
Pulls the current star rating breakdown (e.g., how many 5-star vs. 1-star reviews) for an app.
Retrieves rating data and metrics for a specified time period.
Generates an AI-driven summary of recent user reviews, identifying common themes and sentiment.
Compiles a basic summary of reviews and calculates the average rating.
Fetches a list of individual user reviews for a given app store product.
Ask AI about this MCP
Supported MCP Clients
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019d7550get account check
Verifies the AppFollow account connection status.
019d7550get app info
Retrieves general metadata about a specific app from AppFollow.
019d7550get rankings
Tracks the current store chart rankings for an app.
019d7550get ratings
Gets the current star rating distribution for an app.
019d7550get ratings history
Retrieves rating data for a specific time range.
019d7550get reviews ai summary
Generates an AI summary of the most recent user reviews, highlighting common themes and sentiment.
019d7550get reviews summary
Provides a basic summary of reviews and the overall average rating.
019d7550list reviews
Lists individual user reviews for a specific app store product.
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What you can do with this MCP connector
This MCP Server lets your AI client track app reviews and monitor rankings, pulling real-time data on user feedback and competitor performance across global stores. You'll use it to analyze user feedback, see rating trends, and track your app's visibility against competitors.
get_account_check verifies the AppFollow account connection status.
get_app_info retrieves basic details and metadata for a specific app.
get_rankings tracks the current store chart rankings for an app.
get_ratings pulls the current star rating breakdown for an app.
get_ratings_history retrieves rating data for a specified time period.
get_reviews_ai_summary generates an AI-driven summary of recent user reviews, pointing out common themes and sentiment.
get_reviews_summary compiles a basic summary of reviews and calculates the average rating.
list_reviews fetches a list of individual user reviews for a given app store product.
How AppFollow MCP Works
- 1 First, your agent calls
get_account_checkto verify the AppFollow connection. - 2 Next, you use
get_app_infoto gather baseline metadata about the app or a competitor. - 3 Finally, you call
get_reviews_ai_summarywith the necessary identifiers to get an actionable summary of user sentiment.
The bottom line is: your AI client uses these tools to pull structured data (rankings, ratings, raw reviews) and then synthesizes it into plain language insights about your app's performance.
Who Is AppFollow MCP For?
Product Managers and Growth Marketing specialists need this. They wake up needing to know why their app is losing traction. They can't afford to guess; they need hard data correlating user frustration (reviews) with market performance (rankings). This server gives them the metrics they need to prioritize fixes and marketing pushes.
Uses get_reviews_ai_summary to quickly identify the top three feature requests or bugs mentioned in user reviews, allowing the team to prioritize the next sprint.
Runs get_rankings and get_app_info to measure the effectiveness of new keyword placements and compare current store visibility against top-ranking competitors.
Runs list_reviews and get_reviews_summary to quickly pinpoint recurring user issues, allowing the support team to write better, more targeted help articles.
What Changes When You Connect
- See how your app performs against rivals. Use
get_rankingsandget_app_infoto benchmark your visibility and compare performance metrics directly. - Quickly understand user frustration. Run
get_reviews_ai_summaryto get an immediate, AI-generated summary of user feedback, cutting through thousands of reviews. - Measure rating stability over time.
get_ratings_historypulls historical rating data, showing if a recent update caused a dip or if your rating is trending up. - Identify core product issues.
list_reviewslets you pull raw, specific user reviews. You can then pass that data to your agent for deep analysis of recurring bugs or missing features. - Understand your app's current health.
get_ratingsprovides the current star rating distribution, showing the actual ratio of 5-star to 1-star reviews today. - Automate competitive analysis. By combining
get_app_infoandget_rankings, your agent can build a full competitive profile for a specific app category.
Real-World Use Cases
Analyzing a major feature launch failure
The team just pushed a big update, and the 1-star reviews spiked. Instead of manually reading 500 comments, the Product Manager runs get_reviews_ai_summary. The agent reports that 80% of the negative feedback concerns 'crashes on startup' and 'slow data sync,' immediately telling the dev team what to fix first.
Checking competitor ASO strategy
The ASO Specialist needs to know why a competitor is suddenly ranking higher. They use get_app_info to pull competitor metadata and run get_rankings to track their movement. The agent can then compare this data against your own, suggesting specific keyword gaps to fill.
Validating rating consistency post-update
After releasing a minor patch, the team needs to confirm the rating didn't drop. They run get_ratings_history for the last 30 days and cross-reference the output with get_reviews_summary to ensure the average rating holds steady and that sentiment hasn't dipped.
Identifying niche customer pain points
A support lead gets a flood of tickets about a specific bug. Instead of searching the helpdesk, they run list_reviews for the last month and pass the raw data to the agent. The agent filters and identifies that the issue only affects users on an older version of Android, leading to a targeted fix.
The Tradeoffs
Treating the server like a single dashboard
Calling get_reviews_ai_summary and then ignoring the output, or calling get_ratings but not comparing it to get_reviews_summary's sentiment score. You get data, but no story.
→
Always connect the dots. Run get_app_info first to set context. Then, use get_reviews_ai_summary to understand the why behind the current get_ratings metrics. This combination gives actionable context, not just numbers.
Missing historical context
Running get_rankings today and assuming that's the final word. Rankings fluctuate wildly, and you don't know if the current position is a fluke or a trend.
→
Before making a marketing push, run get_rankings and then compare the output to a longer trend analysis, if available, or cross-reference with get_ratings_history to prove sustained performance.
Over-relying on raw review dumps
Pulling hundreds of raw reviews using list_reviews and asking the agent to 'figure it out.' The agent will fail or give generic answers because the data volume is too high and unstructured.
→
Use get_reviews_ai_summary first. It filters the noise. If you need more detail, use list_reviews for a small, targeted batch of negative reviews, keeping the data size manageable for the agent.
When It Fits, When It Doesn't
Use this server if your primary goal is understanding the relationship between user sentiment and store performance. You need to know why ratings are changing, not just that they are changing. For instance, if you only care about the current number of 5-star reviews, get_ratings is enough. But if you need to know what the 1-star reviews are complaining about, you must use get_reviews_ai_summary and list_reviews together. Don't use this server if you only need simple, static metadata (like a basic company name); use a general metadata API instead. If your only goal is to check if your account is active, just run get_account_check—don't run the full suite.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AppFollow. 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 8 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manually tracking app performance is a full-time job.
Today, tracking your app's reputation means logging into three different dashboards: the App Store Connect dashboard for rankings, a separate analytics tool for rating counts, and a review aggregation platform just to read comments. You spend hours clicking, copy-pasting data points, and trying to manually correlate a drop in visibility with a spike in 1-star reviews. It’s slow, and you always miss the connections.
With the AppFollow MCP Server, your agent handles the whole process. You ask it to 'Summarize why my ranking dropped last week.' It runs `get_rankings`, pulls the associated reviews via `get_reviews_ai_summary`, and delivers a single answer correlating the drop in visibility with a specific bug reported by users.
AppFollow MCP Server: Get instant review insights.
Before, getting a sense of user sentiment required scraping, filtering, and then spending an hour in a spreadsheet to manually tally common complaints. You had to decide if a bug report was a 'feature request' or just a 'user error.'
Now, running `get_reviews_ai_summary` gives you that summary instantly. It tells you, for example, 'Users love X, but 40% of negative feedback points to Y.' That's actionable intelligence, not just data.
Common Questions About AppFollow MCP
How do I use the AppFollow MCP Server to check my account status? +
Run get_account_check. This tool verifies your connection credentials and confirms that your AI client can access the necessary AppFollow data streams.
Which tool should I use to track my app's store rankings? +
Use get_rankings. This tool pulls the current ranking position and historical changes for your app across major store charts.
Can I get a summary of reviews using get_reviews_ai_summary? +
Yes. get_reviews_ai_summary uses AI to analyze recent user feedback, providing a synthesized summary that pinpoints common user issues and overall sentiment.
What is the difference between get_reviews_summary and get_reviews_ai_summary? +
get_reviews_summary gives a basic count and average rating. get_reviews_ai_summary does more; it uses AI to interpret the content and identify themes, which is more actionable for product development.
How do I get historical rating data using get_ratings_history? +
You pass a date range to get_ratings_history. This pulls the star rating distribution metrics and trends for that specific period, allowing you to analyze stability over time.
How do I find detailed app metadata using the get_app_info tool? +
The get_app_info tool retrieves core metadata for any app. You input the app's identifier, and the tool returns details like the developer, category, and release date.
What does the list_reviews tool do, and how do I filter results? +
The list_reviews tool retrieves a full list of user reviews for a product. You can filter the results by date range or star rating within the tool's parameters.
If I need to compare my app against a competitor, which tool should I use? +
Use the get_reviews_ai_summary tool. It processes a large volume of reviews, allowing you to analyze sentiment and identify common complaints across multiple apps or competitors.
How do I find my AppFollow API Token? +
You can find your API token in your AppFollow account settings under the API or Integrations section.
What is an 'extId' in AppFollow? +
The extId is the external ID of the app in the store. For iOS apps, it's the numeric Apple ID. For Google Play apps, it's the bundle name (e.g., com.example.app).
Does this integration support semantic tags? +
Yes, you can use the get_reviews_semantic tool to retrieve reviews categorized by their content type, such as 'Bug', 'Feature Request', or 'Pricing'.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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