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Connect AI Feature Usage 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. Pinpoint exactly how users interact with your AI features.

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.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

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.

A futuristic MCP client docking bay
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 Feature Usage Analytics.

  1. 01

    Get stickiness metrics

    Calculates the stickiness ratio (DAU/MAU) for a specific user segment

  2. 02

    Get usage distribution

    Analyzes the distribution of AI usage intensities across users

  3. 03

    Get engagement trajectory

    Determines the direction and velocity of AI engagement

  4. 04

    Get segment comparison

    Compares AI engagement metrics across different user tiers

Real work

What teams actually run

01

Evaluating Feature Launch Success

After launching a new AI capability, you ask the agent to get_engagement_trajectory to see if the usage is trending up or down month over month.

02

Tiered Pricing Justification

You use get_segment_comparison to show stakeholders that the 'Enterprise' tier has significantly higher AI feature adoption than the 'Basic' tier.

03

Identifying Power Users

You run get_usage_distribution to see if your feature usage is concentrated among a small group of users, signaling a potential bottleneck.

04

Assessing Product Health

You calculate get_stickiness_metrics to determine if the overall user base is relying on the AI features daily or only occasionally.

One connector, every AI

AI Feature Usage 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.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

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

You don't need a client to put AI Feature Usage 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.

AI Feature Usage Analytics is checked daily against the live service.

Daily averagePeak 1019ms
Oct 3Today
Fastest day
729ms
Slowest day
1019ms
14-day trend
Stable0%

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

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

AI Feature Usage Analytics Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_NXLBxo4KbFu77cH9p5U7rwUVSPswlxRdaH1pfsRz/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — AI Feature Usage Analytics capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "ai-feature-usage-analytics-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_NXLBxo4KbFu77cH9p5U7rwUVSPswlxRdaH1pfsRz/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 Feature Usage Analytics in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work AI Feature Usage Analytics owners hand off.

Product Managers and Data Analysts use this MCP to prove the value of their AI features. If you need to move beyond basic metrics and understand true user habit formation, this is for you. It gives your agent the data to justify roadmap decisions.

  • 01

    Product Manager

    Determines if AI features are sticky enough to justify continued development investment.

  • 02

    Data Analyst

    Calculates and compares usage metrics across different user segments and time periods.

  • 03

    SaaS Founder

    Gets a clear picture of product health by analyzing overall feature adoption and engagement trajectory.

FAQ

Questions AI Feature Usage Analytics owners ask.

  • 01

    Does this MCP track all AI feature usage?

    The MCP provides deep insights into how users interact with AI capabilities. It is designed to calculate metrics like stickiness and usage distribution for the features you track.

  • 02

    What is the difference between stickiness and engagement?

    Stickiness measures the ratio of daily to monthly active users for a segment. Engagement trajectory, however, tracks the overall direction and velocity of user interest over time.

  • 03

    Can I compare different user groups?

    Yes. You can use the get_segment_comparison capability to compare AI engagement metrics across different user tiers, helping you spot performance gaps.

  • 04

    Is this only for large companies?

    No. It helps any product team analyze their AI features. You can analyze usage intensity and engagement trends regardless of your company size.

  • 05

    How is stickiness calculated?

    Stickiness is calculated using the get_stickiness_metrics capability, which divides daily active users by monthly active users.

  • 06

    Can I compare different user tiers?

    Yes, use get_segment_comparison to compare average sessions and stickiness between segments like free, pro, or enterprise.

  • 07

    What kind of usage distribution data is available?

    The get_usage_distribution capability categorizes users into low, medium, and high-frequency buckets based on their session counts.

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