Does this MCP tell me why users are unhappy with AI?

It helps you pinpoint the risk. The evaluate_detractor_risk capability predicts the likelihood of becoming a detractor based on specific AI-related grievances, giving you the key areas to investigate.

Can I use this to compare AI impact across different user types?

Yes. You can use aggregate_segment_impact to get a high-level summary of how AI affects different user demographics, letting you compare groups easily.

What kind of data does it need to calculate NPS lift?

The calculate_nps_lift capability requires comparison data, specifically the NPS score before the AI feature was used and the score after its implementation.

Is this just a dashboard, or does it give me actionable metrics?

It's an analytics engine, not a dashboard. It gives you actionable metrics, like a quantified Value Perception score, which you can use directly in your product roadmap.

How does the engine calculate NPS lift?

The calculate_nps_lift capability calculates the absolute difference between the NPS of users interacting with the AI feature and the baseline NPS of users who do not.

Can I predict user churn using this capability?

Yes, by using evaluate_detractor_risk, you can predict the likelihood of users becoming detractors based on their AI satisfaction and specific grievances.

What is the purpose of quantifying feature value?

The quantify_feature_value capability translates NPS and satisfaction data into a business-centric metric that categorizes the feature as Essential, Nice-to-have, or a Commodity.

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Connect AI Feature NPS Impact Engine to ChatGPT and Claude

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Understand if AI drives loyalty or creates friction.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 4 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-04

4 capabilities in this set.

Part of 4 available through AI NPS Impact Engine.

01
Calculate nps lift
Determines how much the AI feature is driving or dragging the overall product NPS
02
Quantify feature value
Translates NPS and satisfaction data into a business-centric Value Perception metric
03
Aggregate segment impact
Provides a high-level summary of how AI is impacting different user demographics
04
Evaluate detractor risk
Predicts the likelihood of users becoming detractors based on their specific AI-related grievances

Real work

What teams actually run

Post-Launch Feature Review

After rolling out a new AI feature, use this MCP to immediately calculate the NPS lift and see if the launch was successful.

Identifying User Pain Points

Run the detractor risk capability to find out if users are complaining about specific AI behaviors, like hallucinations.

Prioritizing Development

Aggregate segment impact data to decide which user group needs the most attention or which AI feature should be built next.

Stakeholder Reporting

Use the feature value capability to present a clear, quantified business case for continued AI investment to executives.

One connector, every AI

AI NPS Impact Engine works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

Building your own app? The connector is yours to use.

You don't need a client to put AI NPS Impact Engine to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

850ms average. Fast in production.

AI NPS Impact Engine is checked daily against the live service.

Daily averagePeak 1161ms

Oct 3Today

Fastest day: 690ms
Slowest day: 1161ms
14-day trend: Slowing+15%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 4 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of AI NPS Impact Engine, so you can see the experience inside your AI.

It does not authenticate your account with AI NPS Impact Engine. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

AI NPS Impact Engine Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_jHkDc4P6O0vpL4pfOavrHt2AJqoEz61FC4hG6fkc/mcp

Claude Desktop

Follow the steps below to connect in seconds.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3Click Add and start a new chat — AI NPS Impact Engine capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "ai-feature-nps-impact-engine-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_jHkDc4P6O0vpL4pfOavrHt2AJqoEz61FC4hG6fkc/mcp"
    }
  }
}

Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up AI NPS Impact Engine in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work AI NPS Impact Engine owners hand off.

Product Managers, Data Scientists, and Product Owners use this MCP to prove the ROI of their AI investments. If you're responsible for user retention or product loyalty, this capability gives you the hard numbers you need to make development decisions.

01
Product Manager
Determines if an AI feature is a core driver of loyalty or if it's creating user friction.
02
Data Scientist
Calculates the precise NPS lift and identifies high-risk user segments based on AI grievances.
03
Product Owner
Translates complex satisfaction data into a clear, actionable business value metric.

FAQ

Questions AI NPS Impact Engine owners ask.

01
Does this MCP tell me why users are unhappy with AI?
It helps you pinpoint the risk. The evaluate_detractor_risk capability predicts the likelihood of becoming a detractor based on specific AI-related grievances, giving you the key areas to investigate.
02
Can I use this to compare AI impact across different user types?
Yes. You can use aggregate_segment_impact to get a high-level summary of how AI affects different user demographics, letting you compare groups easily.
03
What kind of data does it need to calculate NPS lift?
The calculate_nps_lift capability requires comparison data, specifically the NPS score before the AI feature was used and the score after its implementation.
04
Is this just a dashboard, or does it give me actionable metrics?
It's an analytics engine, not a dashboard. It gives you actionable metrics, like a quantified Value Perception score, which you can use directly in your product roadmap.
05
How does the engine calculate NPS lift?
The calculate_nps_lift capability calculates the absolute difference between the NPS of users interacting with the AI feature and the baseline NPS of users who do not.
06
Can I predict user churn using this capability?
Yes, by using evaluate_detractor_risk, you can predict the likelihood of users becoming detractors based on their AI satisfaction and specific grievances.
07
What is the purpose of quantifying feature value?
The quantify_feature_value capability translates NPS and satisfaction data into a business-centric metric that categorizes the feature as Essential, Nice-to-have, or a Commodity.

Explore

More in Product Management

Connect AI NPS Impact Engine to Claude, Cursor, ChatGPT & more

Your AI does the work. We keep it safe and sound.

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01-04

Calculate nps lift

Quantify feature value

Aggregate segment impact

Evaluate detractor risk

What teams actually run

Post-Launch Feature Review

Identifying User Pain Points

Prioritizing Development

Stakeholder Reporting

Building your own app? The connector is yours to use.

AI NPS Impact Engine Connector

Claude Desktop

Product Manager

Data Scientist

Product Owner

Does this MCP tell me why users are unhappy with AI?

Can I use this to compare AI impact across different user types?

What kind of data does it need to calculate NPS lift?

Is this just a dashboard, or does it give me actionable metrics?

How does the engine calculate NPS lift?

Can I predict user churn using this capability?

What is the purpose of quantifying feature value?

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