AI Engagement ScoringをChatGPTとClaudeに接続

最速日

693ms

最遅日

1215ms

14日間の傾向

遅延傾向+41%

AI Engagement Scoring Connector

準備完了です。MCPクライアントを選択し、セットアップ手順に従ってください。

コネクタリンクhttps://edge.vinkius.com/vk_preview_4wOGVgGo4t204rkw2C4D4i8XjePsZ299pNnX5ev6/mcpコピー

Claude Desktop

以下の手順で数秒で接続できます。

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 Engagement Scoring capabilities are ready to use.

設定 · claude_desktop_config.jsonコピー

{
  "mcpServers": {
    "ai-engagement-scoring-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_4wOGVgGo4t204rkw2C4D4i8XjePsZ299pNnX5ev6/mcp"
    }
  }
}

• Claude
• ChatGPT
• Cursor
• VS Code
• Windsurf
• Claude Code
• JetBrains
• Cline

各クライアントの手順ガイドはこちらです。 接続方法

Claude向けのガイド付きセットアップ？ AI Engagement ScoringをClaudeや他のAIに設定する

このConnectorが対応するすべてのAIクライアントを見る ↑

• 01

  プロダクトマネージャー

  エンゲージメントスコアや機能採用率を把握し、製品ロードマップの改善点を見つけ出すことができます。

• 02

  データアナリスト

  ユーザーの行動パターンを詳細に分析し、離脱の兆候や利用のボトルネックを特定するのに役立ちます。

• 03

  グロースマーケター

  どのユーザー層が価値を失いつつあるかを予測し、適切なタイミングでリテンション施策を打つための根拠を得られます。

• 01

  Does this MCP track all user activity?

  No, it focuses specifically on AI feature engagement. It helps you measure how users interact with your AI capabilities, providing metrics on adoption and value realization.

• 02

  Can I use this for multiple product lines?

  The MCP is designed to analyze AI feature engagement. You must ensure the data source feeding the MCP covers all the AI features you want to measure.

• 03

  What is the difference between engagement and adoption?

  Adoption metrics show if users are finding and trying a feature. Engagement scores give a holistic view of how valuable and active the user is with the product overall.

• 04

  Is this only for large companies?

  No. This MCP helps any product team that needs to understand user retention and feature stickiness, regardless of company size.

• 05

  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.

• 06

  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.

• 07

  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.

• 

  AI Feature Adoption Analytics AIコネクタ

  Calculate AI feature adoption rates, velocity, stickiness, and funnel efficiency.

  表示
• 

  Enterprise User Adoption Analytics AIコネクタ

  Monitor and forecast software adoption metrics for enterprise deployments.

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• 

  Enterprise Email Engagement Analytics AIコネクタ

  Analyze email campaign performance, response rates, and engagement drivers.

  表示
• 

  Churn Rate Calculator AIコネクタ

  Calculate Customer Churn Rate, Revenue Churn Rate, and Net Revenue Retention (NRR) to instantly assess account

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