AI Feature Discovery AnalyticsをAIに接続

最速日

631ms

最遅日

1073ms

14日間の傾向

遅延傾向+8%

AI Feature Discovery Analytics 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_M2ugyR0bdwPEifqVrgkiN2rOjMAmxmSaK4RbYzhA/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 Feature Discovery Analytics capabilities are ready to use.

設定 · claude_desktop_config.jsonコピー

{
  "mcpServers": {
    "ai-feature-discovery-analytics-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_M2ugyR0bdwPEifqVrgkiN2rOjMAmxmSaK4RbYzhA/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 Feature Discovery Analytics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

  AI機能の市場適合性を測定し、ローンチ後のKPI達成に向けた戦略策定を行います。

• 02

  SaaSプロダクトオーナー

  ユーザー体験（UX）のボトルネックを特定し、プロダクトの成長とエンゲージメント率の向上を目的とします。

• 03

  データアナリスト

  発見率、チャネルごとの貢献度など、多角的なユーザー行動データを深く分析し、レポートを作成します。

• 01

  Does this MCP work for any type of feature, or just AI features?

  This MCP is specialized for measuring AI features within a SaaS product. It focuses on the unique challenges of user discovery and adoption for advanced, AI-driven capabilities.

• 02

  What kind of data does the MCP need to run?

  The MCP needs usage data, including metrics like user counts, feature interaction counts, and timestamps. The prompt examples show how to provide this raw data for calculation.

• 03

  Can I use this to compare different product versions?

  Yes. You can input data from different time periods or product versions to compare performance, helping you see if a change improved the discovery rate.

• 04

  Is this MCP just a report generator, or does it give advice?

  It does both. After calculating metrics, the MCP can use the generate_acceleration_strategy capability to provide concrete, actionable advice on how to improve adoption.

• 05

  What is the discovery rate?

  The discovery rate is the percentage of users who successfully identified and attempted to use an AI feature out of the total group exposed to it, calculated via calculate_discovery_metrics.

• 06

  How can I improve my feature adoption?

  You can use generate_acceleration_strategy to receive specific, actionable advice based on your current discovery rate, UI friction, and promotion intensity.

• 07

  Can I compare different marketing channels?

  Yes, the evaluate_channel_performance capability allows you to compare the effectiveness of various channels like Modals, Tooltips, or Email.

• 

  AI Feature Adoption Analytics AIコネクタ

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

  表示
• 

  AI Power User Analytics Engine AIコネクタ

  Quantify AI power user density, value multipliers, and feature depth.

  表示
• 

  Feature Adoption Analytics AIコネクタ

  Quantify feature success through adoption velocity, saturation estimates, and retention impact modeling.

  表示
• 

  Enterprise User Adoption Analytics AIコネクタ

  Monitor and forecast software adoption metrics for enterprise deployments.

  表示

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