How do I use this MCP with Claude?

You connect to the MCP through the Vinkius platform. Once connected, your Claude client can immediately call the capabilities to analyze your product data.

What kind of data do I need to provide?

You provide usage counts, retention percentages, or user frequency logs. The AI uses this data to run the specific analytics capabilities.

Can I use this to predict future growth?

Yes. You can use the simulate_growth_scenarios capability to model how changing product variables will affect your future adoption.

Does this replace my existing analytics dashboard?

It acts as an intelligence layer on top of your data. It's designed to turn raw numbers into specific insights like retention lift or user segmentation.

Vinkius hosts and manages the MCP, ensuring your connection to your AI client is stable and secure.

How can I predict when a feature will reach its peak usage?

You can use the calculate_adoption_metrics capability, which provides a saturationEstimate representing the predicted number of days until the adoption rate levels off.

Can I see if a feature is actually helping with user retention?

Yes, the evaluate_retention_impact capability compares the retention rates of users who adopted the feature against those who did not, providing a retention lift and an impact category.

How do I model the effect of better onboarding?

Use the simulate_growth_scenarios capability. You can input a proposedOnboardingImprovement to see how it affects the projected adoption rate and the time needed to reach targets.

An astronaut overseeing a futuristic data hub

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Connect Feature Adoption Analytics 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. Turn raw usage data into clear product growth models.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

Waiting for input…

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 Feature Adoption Analytics.

01
Predict usage segmentation
This capability categorizes your user base into usage tiers to separate power users from casual ones.
02
Calculate adoption metrics
This capability provides a snapshot of current adoption progress and projects the feature's future trajectory.
03
Simulate growth scenarios
This capability lets you model how adjusting product variables affects future adoption rates.
04
Evaluate retention impact
Use this to determine if a specific feature is a primary driver of user loyalty.

Real work

What teams actually run

Post-Launch Validation

Check if a new feature is actually being adopted as expected after the first few days of release.

Retention Audits

Determine if a specific feature is a key reason why users stay with your product.

User Segmentation

Identify your power users to better understand who is getting the most value from your updates.

Growth Projections

Run simulations to see how improving feature discoverability might change your adoption trajectory.

One connector, every AI

Feature Adoption Analytics 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 Feature Adoption Analytics 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

888ms average. Fast in production.

Feature Adoption Analytics is checked daily against the live service.

Daily averagePeak 1031ms

Oct 3Today

Fastest day: 722ms
Slowest day: 1031ms
14-day trend: Slowing+7%

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 Feature Adoption Analytics, so you can see the experience inside your AI.

It does not authenticate your account with Feature Adoption Analytics. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Feature Adoption Analytics Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_Le74ELteNgoiGDAC6NxIt3AoKsK63ogoqWJr21tq/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 — Feature Adoption Analytics capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "feature-adoption-analytics-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_Le74ELteNgoiGDAC6NxIt3AoKsK63ogoqWJr21tq/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 Feature Adoption Analytics in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Feature Adoption Analytics owners hand off.

This MCP is built for product teams who need to move fast without losing sight of user behavior.

01
Product Managers
You hand off raw usage data to the AI to get immediate adoption and retention insights.
02
Product Analysts
You use the capability to run complex growth simulations and segmentation models.
03
Growth Leads
You use the modeling capabilities to test how onboarding changes might affect long-term growth.

FAQ

Questions Feature Adoption Analytics owners ask.

01
How do I use this MCP with Claude?
You connect to the MCP through the Vinkius platform. Once connected, your Claude client can immediately call the capabilities to analyze your product data.
02
What kind of data do I need to provide?
You provide usage counts, retention percentages, or user frequency logs. The AI uses this data to run the specific analytics capabilities.
03
Can I use this to predict future growth?
Yes. You can use the simulate_growth_scenarios capability to model how changing product variables will affect your future adoption.
04
Does this replace my existing analytics dashboard?
It acts as an intelligence layer on top of your data. It's designed to turn raw numbers into specific insights like retention lift or user segmentation.
05
Is my data secure?
Vinkius hosts and manages the MCP, ensuring your connection to your AI client is stable and secure.
06
How can I predict when a feature will reach its peak usage?
You can use the calculate_adoption_metrics capability, which provides a saturationEstimate representing the predicted number of days until the adoption rate levels off.
07
Can I see if a feature is actually helping with user retention?
Yes, the evaluate_retention_impact capability compares the retention rates of users who adopted the feature against those who did not, providing a retention lift and an impact category.
08
How do I model the effect of better onboarding?
Use the simulate_growth_scenarios capability. You can input a proposedOnboardingImprovement to see how it affects the projected adoption rate and the time needed to reach targets.

Explore

More in Product Management

Connect Feature Adoption Analytics 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

Predict usage segmentation

Calculate adoption metrics

Simulate growth scenarios

Evaluate retention impact

What teams actually run

Post-Launch Validation

Retention Audits

User Segmentation

Growth Projections

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

Feature Adoption Analytics Connector

Claude Desktop

Product Managers

Product Analysts

Growth Leads

How do I use this MCP with Claude?

What kind of data do I need to provide?

Can I use this to predict future growth?

Does this replace my existing analytics dashboard?

Is my data secure?

How can I predict when a feature will reach its peak usage?

Can I see if a feature is actually helping with user retention?

How do I model the effect of better onboarding?

AI Feature Adoption Analytics AI Connector

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