Make your AI work with AI Engagement Scoring
Connect your account once and let the AI you already use work with it, without building another integration or switching to a different AI. Predicting User Value and Feature Adoption in SaaS Products
4 live capabilities. One account. Your AI. Real work.
- Step 01
Connect
Link your account through Vinkius.
- Step 02
Authorize
You decide what your AI can access.
- Step 03
Pick your AI
Use it with the AI application you already use.
- Step 04
Get things done
Ask your AI to work with your connected account.
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Works with
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Waiting for input…
Why people use AI Engagement Scoring
AI Engagement Scoring for Tracking User Feature Adoption
With this MCP, you ask your agent to evaluate feature adoption metrics directly. You get a single, clear answer showing the difference between how many people saw the feature and how many actually found consistent value. It cuts out the spreadsheet work and gives you immediate, actionable data.
What Vinkius changes
The bottom line is, you get predictive user health metrics, not just raw usage numbers.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 7,300+ Connectors
- Real-world use case 01
A new feature is getting ignored.
A Product Manager runs into a wall of low adoption rates.
- Real-world use case 02
A key client suddenly went quiet.
A Growth Lead notices a high-value client hasn't logged in for a week.
- Real-world use case 03
Need to prove product stickiness.
A PM wants to show investors that the product is sticky.
Complete set · 4capabilities
The complete AI Engagement Scoring capability set.
These are the exact actions your AI can choose when you ask it to work with AI Engagement Scoring.
01—04
4 capabilities in this set.
Part of 4 available through AI Engagement Scoring.
- 01 Capability
Analyze engagement trend
Tracks a user's interest in your AI features over time, showing if their usage is increasing or dropping. This helps you spot declining interest before it becomes a retention problem.
- 02 Capability
Calculate user engagement score
Provides a single, current score for any user, giving you an immediate measure of their overall value realization in your product.
- 03 Capability
Get feature adoption metrics
Evaluates how successfully your users are discovering and adopting specific AI features. This helps you pinpoint which features need better visibility or onboarding.
- 04 Capability
Predict user churn risk
Identifies users who are statistically likely to stop using your AI features. This allows your team to intervene with targeted support or product updates.
Set up in minutes
One URL. Then ask AI Engagement Scoring to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use AI Engagement Scoring from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_4wOGVgGo4t204rkw2C4D4i8XjePsZ299pNnX5ev6/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it AI Engagement Scoring, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable AI Engagement Scoring for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_4wOGVgGo4t204rkw2C4D4i8XjePsZ299pNnX5ev6/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the AI Engagement Scoring URL.
- Step 03
Save and start
Save the connection and enable AI Engagement Scoring in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"ai-engagement-scoring": {
"url": "https://edge.vinkius.com/vk_preview_4wOGVgGo4t204rkw2C4D4i8XjePsZ299pNnX5ev6/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using AI Engagement Scoring
Open Agent mode in chat and ask: "Using AI Engagement Scoring, help me...". 4 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"ai-engagement-scoring": {
"url": "https://edge.vinkius.com/vk_preview_4wOGVgGo4t204rkw2C4D4i8XjePsZ299pNnX5ev6/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using AI Engagement Scoring
Ask Copilot: "Using AI Engagement Scoring, help me...". 4 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"ai-engagement-scoring": {
"url": "https://edge.vinkius.com/vk_preview_4wOGVgGo4t204rkw2C4D4i8XjePsZ299pNnX5ev6/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using AI Engagement Scoring
Open Cascade and ask: "Using AI Engagement Scoring, help me...". 4 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"ai-engagement-scoring": {
"url": "https://edge.vinkius.com/vk_preview_4wOGVgGo4t204rkw2C4D4i8XjePsZ299pNnX5ev6/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using AI Engagement Scoring
Ask Cline: "Using AI Engagement Scoring, help me...". 4 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add ai-engagement-scoring --transport http "https://edge.vinkius.com/vk_preview_4wOGVgGo4t204rkw2C4D4i8XjePsZ299pNnX5ev6/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using AI Engagement Scoring
Ask Claude: "Using AI Engagement Scoring, show me...". 4 tools are ready
Where the request belongs
Work AI Engagement Scoring can move forward.
Product Managers and Growth Leads need this. If you're tired of looking at dashboards filled with vanity metrics (like total clicks) and can't tell if your users are actually finding lasting value, this is for you. It gives you the predictive power to act before users leave.
Product Manager
Uses this to determine if a new feature is sticky or if users are just testing it. They use the metrics to prioritize the next set of product improvements.
Growth Lead
Uses this to identify high-value users who are slipping away, allowing them to launch targeted re-engagement campaigns.
Data Analyst
Uses this to build predictive models, moving beyond simple reporting to forecast future user behavior and product health.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse Connectors
AI SaaS Feature Churn Correlation
Quantify the impact of AI features on customer retention and calculate prevented churn revenue.
AI Feature Retention Analyzer
Quantify the impact of AI features on user retention and ROI.
AI Feature Value Realization
Quantify the time to value and adoption efficiency of your AI features.
AI Feature Expansion Impact Analyzer
Quantify the financial and behavioral impact of AI features on SaaS expansion revenue and upsell conversion.
AI Model Usage Analytics
Analyze AI model cost distribution and usage concentration across product features.
AI Feature Upsell Correlation
Quantify the impact of AI features on subscription upgrades.
Bring your own AI
Change the model, client or framework. Keep AI Engagement Scoring connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about AI Engagement Scoring.
The practical details behind the request, access and result.
How do I use AI Engagement Scoring MCP to measure if my users are actually getting value?
It measures value by calculating a holistic score that goes beyond simple clicks. You use the MCP to get a single metric that factors in feature realization and overall usage patterns, telling you if the user is truly integrated into your workflow.
Does AI Engagement Scoring MCP help me predict which users will leave?
Yes, the MCP includes a predictive capability that flags users at risk of churning. It analyzes declining trends and low scores, giving you a warning flag weeks before they stop using your product.
What is the difference between simple analytics and using AI Engagement Scoring MCP?
Simple analytics just counts actions (e.g., 100 clicks). This MCP tells you why those actions matter. It distinguishes between simple feature testing and true, sustained value realization in your product.
Can I use AI Engagement Scoring MCP to guide my product roadmap?
Absolutely. By running feature adoption metrics, you can pinpoint which features are being discovered but not realized. This tells you exactly where to focus your development efforts for maximum impact.
Is AI Engagement Scoring MCP better than just looking at monthly active users?
Yes. Monthly active users only tells you if they logged in. The MCP tells you if they used the product's core AI features and if that usage is trending up or down, which is a much stronger indicator of health.
How is the engagement score calculated?
The score is determined by calculate_user_engagement_score, which evaluates session volume, feature breadth, and the ratio of successful value realizations to total AI outputs.
Can I predict which users might stop using AI features?
Yes, you can use predict_user_churn_risk to identify users showing declining engagement trends and low value realization.
How do I measure if a new AI feature is successful?
Use get_feature_adoption_metrics to compare the discovery rate against the realization rate, which measures how many users actually find value in the feature.
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