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ClaudeChatGPTPerplexityGeminiMicrosoft CopilotRaycastMeta AIGrokZ.aiQwenKimi
DeepSeekMistralCursorVS CodeWindsurfJetBrainsClineLovableVercel AI SDKLangChain

Use AI Feature Usage Analytics with your AI.

Connect your account once and let the AI you already use work with it, without building another integration. Pinpoint exactly how users interact with your AI features.

Included with plan

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

Complete set · 4 capabilities

The complete AI Feature Usage Analytics capability set.

These are the exact actions your AI can choose when you ask it to work with AI Feature Usage Analytics.

Capability set01 / 01

01-04

4 capabilities in this set.

Part of 4 available through AI Feature Usage Analytics.

  1. 01

    Get engagement trajectory

    Determines the direction and velocity of AI engagement

  2. 02

    Get segment comparison

    Compares AI engagement metrics across different user tiers

  3. 03

    Get stickiness metrics

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

  4. 04

    Get usage distribution

    Analyzes the distribution of AI usage intensities across users

Observed, not estimated

878ms average. Fast in production.

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

Daily averagePeak 1027ms
Aug 28Today
Fastest day
786ms
Slowest day
1027ms
14-day trend
Improving-18%

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

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.

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