AppFollow MCP. Analyze App Store Sentiment and Rankings
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AppFollow handles app store reputation management by tracking sentiment, analyzing user reviews, and monitoring ranking changes across major platforms. It gives your agent intelligence on user feedback and market positioning, letting you quickly see if a new feature is causing issues or if your ASO efforts are paying off.
What your AI agents can do
Get account check
Verifies that the AppFollow account connection is active and authorized.
Get app info
Retrieves basic metadata about a specific app from the platform.
Get rankings
Tracks your app's current and historical position in store charts.
The agent generates summaries and analyzes raw reviews to determine overall user mood (positive, negative, neutral).
You can monitor how your app's ranking changes in store charts day-by-day.
The agent lists and searches for specific user reviews across different regions and languages.
You retrieve historical data to see how star ratings have changed over a long period.
The agent compares your app's performance metrics against global store data from rivals.
Ask AI about this MCP
Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
AppFollow: 8 Tools for Market Intelligence
These eight tools let you retrieve everything from raw user reviews to historical rating trends, giving your agent a complete view of app performance and market standing.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using AppFollow on Vinkius019d7550get account check
Verifies that the AppFollow account connection is active and authorized.
019d7550get app info
Retrieves basic metadata about a specific app from the platform.
019d7550get rankings
Tracks your app's current and historical position in store charts.
019d7550get ratings
Provides a snapshot of the current star rating distribution for your app.
019d7550get ratings history
Retrieves detailed records showing how ratings have changed over time.
019d7550get reviews ai summary
Generates a high-level, AI-powered summary of the most recent user reviews and feedback themes.
019d7550get reviews summary
Provides a quick overview of overall review sentiment and average ratings for an app.
019d7550list reviews
Lists individual user reviews for a specific product, allowing filtering by store or language.
Choose How to Get Started
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Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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Make Your AI Do More
Start with AppFollow, then connect any of our 4,800+ other servers whenever your AI needs more. One click, no limits.
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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.
Manually tracking reputation across apps is exhausting.
Right now, managing app health means hopping between multiple dashboards. You're checking one tab for star ratings, another dashboard for current chart rankings, and then opening a third portal just to read the raw reviews. It’s a painful cycle of copying data points into a spreadsheet, trying to stitch together what a 3-star rating from three weeks ago actually meant in the context of today's complaints.
With this MCP, your agent pulls all that messy data—the historical trends, the current rankings, and the raw text feedback—into one conversation. You don't copy anything; you just ask, 'What is our reputation right now?' And it tells you.
You get a full picture of user sentiment.
The biggest manual step that disappears is the need to manually read through dozens of reviews just to categorize complaints. You don't have to spend an hour reading 50-star reviews; you simply ask for an AI summary, and it tells you, 'Three people complained about login flow.'
Now your team gets a single source of truth, making decision-making instantaneous. Your agent doesn't just give data points; it gives insights.
What you can do with this MCP connector
This MCP connects powerful app store data directly to your AI client. Instead of manually checking multiple dashboards for star ratings, chart positions, and scattered reviews, your agent pulls everything into one place. You can get an instant summary of user sentiment from recent feedback or dive deep into historical rating trends over months.
If you're trying to understand why adoption slowed down last quarter, you don't have to guess; you just ask the agent to compare your current performance against what competitors are doing. Everything is managed through Vinkius, making it simple for any MCP-compatible client to access this market intelligence. You can use the AI summary of user feedback to pinpoint common bugs or feature requests immediately, letting product managers prioritize their backlogs by actual user pain points.
019d7550-1859-712d-b600-364d89d63c3a 
How AppFollow MCP Works
- 1 First, you run
get_account_checkto ensure the MCP is connected to your AppFollow credentials. - 2 Next, tell your agent what you need—for instance, 'What were my average ratings last quarter?' The agent then runs a historical tool like
get_ratings_historyor pulls current data usingget_reviews_summary. - 3 Finally, the MCP delivers the requested structured data, allowing you to immediately analyze trends, spot negative feedback patterns, or see how your app ranks compared to competitors.
The bottom line is that the agent abstracts away complex API calls, letting you focus only on interpreting the business outcome.
Who Is AppFollow MCP For?
Product Managers and Growth Marketers. You're the one who wakes up at 2 AM when a competitor suddenly jumps in rankings or when review complaints spike unexpectedly. You need to know why your users are unhappy, not just that they are.
Using the AI summary of user feedback and reviewing detailed list_reviews output to identify common bugs and prioritize feature requests.
Running trend analyses with get_rankings and comparing metrics against competitors to measure ASO/UA campaign effectiveness.
Checking reviews using list_reviews or running a full AI summary of user feedback to quickly identify systemic issues needing documentation updates.
What Changes When You Connect
- Stop guessing about user pain points. Use
get_reviews_ai_summaryto get immediate, AI-generated summaries of feedback, letting you understand why users are frustrated without reading hundreds of reviews. - Measure your market impact accurately. Running
get_rankingsallows you to see if a recent marketing push actually moved the needle in store visibility charts. - Track reputation shifts over time. Instead of relying on gut feeling, use
get_ratings_historyto visualize long-term trends and spot when rating decay began. - Understand your competitive standing immediately. You can compare performance against rivals using global data, giving you clear targets for ASO improvements.
- Get deep context from raw feedback. When the summary isn't enough,
list_reviewslets your agent pull specific, filtered user comments, so you never miss a key bug report.
Real-World Use Cases
Investigating a sudden rating drop
A PM notices the 4.5-star average dropped to 3.8 last week. They ask their agent to check get_ratings_history first, then use list_reviews to pull recent 1- and 2-star reviews. The agent quickly surfaces that most complaints relate to a failure after the latest update, directing immediate engineering focus.
Assessing new feature adoption
A Growth Marketer launches a major UI overhaul. They run get_reviews_ai_summary on feedback received in the last 7 days. The agent summarizes that while users love the look, they are confused by the placement of the settings menu, allowing the PM to adjust UX before the next release.
Pre-launch competitive check
Before launching in a new country, a marketer runs get_app_info and uses get_rankings for local competitors. This gives them data on naming conventions or popular feature sets they should incorporate to stand out.
Quarterly performance review
A product owner needs a high-level view of the last quarter's success. They run get_reviews_summary for an overall score and then use get_ratings_history to show stakeholders a clear, defensible line graph of reputation growth.
The Tradeoffs
Treating reviews as one big pool
Asking the agent for 'all feedback' without specifying timeframes or star ratings. The resulting data dump is massive, unmanageable, and requires hours of manual sorting.
→
Always narrow your scope. If you want bug reports, use list_reviews and filter by 1-star reviews from the last month. If you just need a general idea, run get_reviews_ai_summary first.
Missing historical context
Seeing low ratings today and assuming it’s always been this bad. You miss the fact that the rating was actually high six months ago.
→
Before making any judgment, check get_ratings_history. This tool shows you the full trajectory of your app's reputation, not just a single point in time.
Ignoring competitor data
Thinking your app is doing fine because your metrics look good. But what if two key rivals are suddenly ranking higher?
→
Run get_rankings and use it to compare multiple apps against each other on the same chart. This gives you external context for internal performance dips.
When It Fits, When It Doesn't
Use this MCP if your core problem is understanding why users feel a certain way about your app or where your app stands in its market category. You need to combine qualitative (review text) and quantitative (ratings/rankings) data sources into one actionable report. Use get_reviews_ai_summary when you're on the clock and need quick thematic insights. However, if you are only concerned with basic metadata or just listing reviews without context, a simple dedicated store API might suffice. Don't use this MCP if your goal is solely to calculate complex financial projections; for that, you need a specialized finance tool instead.
Common Questions About AppFollow MCP
How do I check my app’s current rating using get_ratings? +
Run get_ratings to instantly see the distribution of star ratings. This gives you a snapshot of your overall health right now.
Can I find out why rankings dropped with get_rankings? +
Yes, running get_rankings provides historical data that lets you track visibility changes and pinpoint when the drop occurred relative to other market events.
What's the difference between get_reviews_summary and get_reviews_ai_summary? +
Use get_reviews_summary for a quick, quantitative average rating. Use get_reviews_ai_summary when you need qualitative insight—the AI pulls out the actual themes people are discussing.
How do I check competitor performance? +
Use get_app_info to retrieve metadata for competitors and then use get_rankings across multiple apps to benchmark your position against them.
How do I check if my AppFollow connection credentials are working with get_account_check? +
The tool immediately verifies your account status. It confirms you have active, authorized access to all required app store data streams. This ensures that any subsequent calls for reviews or rankings won't fail due to an expired connection.
Can I use list_reviews to filter user feedback by a specific country? +
Yes, the tool allows you to specify the target market. You can restrict the review listing to particular countries or regions. This means your AI agent analyzes exactly the feedback relevant to that localized audience.
What key details does get_app_info provide about my product? +
It pulls basic metadata for your app from the store. You get core information like the official name, developer ID, and general category details. This context helps ground analysis when you're comparing performance or summarizing reviews.
How does get_ratings_history help me understand long-term rating trends? +
This function tracks data over specific time ranges you define. You provide a start and end date, giving your agent a detailed visualization of how your average star rating has changed historically.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.