AI Model Usage AnalyticsをChatGPTとClaudeに接続

最速日

656ms

最遅日

1043ms

14日間の傾向

安定-3%

AI Model Usage 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_1tbd24ReYBC3SDpWJ579WdByT3lsZc0AwncKcSyG/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 Model Usage Analytics capabilities are ready to use.

設定 · claude_desktop_config.jsonコピー

{
  "mcpServers": {
    "ai-model-usage-analytics-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_1tbd24ReYBC3SDpWJ579WdByT3lsZc0AwncKcSyG/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 Model Usage 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

  どの機能にリソースを割くべきか、コスト効率の高い機能改善点を特定できます。

• 02

  CTO/技術責任者

  AIインフラ全体の利用状況を把握し、技術的なボトルネックやコスト増大の根本原因を特定できます。

• 03

  FinOpsエンジニア

  AIモデルの利用に伴う費用を機能単位で正確に算出し、予算管理とコスト削減の根拠を提供できます。

• 01

  Does this MCP track costs for every single AI model I use?

  Yes. This MCP provides deep visibility into how AI model resources are consumed across your product, allowing you to calculate cost attribution per feature.

• 02

  What is the difference between usage concentration and cost breakdown?

  Usage concentration tells you which features are the primary drivers of overall AI model calls. The cost breakdown gives you the exact monetary expenditure for each individual feature.

• 03

  Can I use this with my existing AI stack?

  Since this MCP is hosted on Vinkius, you connect your preferred AI client—like Claude, Cursor, or Windsurf—once, and you get access to all the analytics capabilities.

• 04

  Is this only for cost tracking, or can it help with efficiency?

  It does both. You can find cost-saving opportunities by identifying optimization targets, or you can evaluate model selection effectiveness using the routing efficiency score.

• 05

  How does this capability help reduce AI costs?

  By using identify_optimization_targets, you can find features where high costs don't match user engagement, allowing you to switch to more efficient models.

• 06

  Can I see which features are using the most models?

  Yes, the analyze_usage_concentration capability identifies which features are the primary drivers of AI model consumption.

• 07

  How is routing efficiency measured?

  The get_routing_efficiency_score capability compares the actual cost incurred by a feature against the theoretical minimum cost of using the most efficient model for that task.

• 

  AI Feature Usage Analytics AIコネクタ

  Analyze AI feature stickiness, usage distribution, and engagement trajectories.

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• 

  AI Model Versioning Cost AIコネクタ

  Calculate the financial and operational impact of managing AI model versions.

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• 

  AI Feature Flag Cost & Risk Modeler AIコネクタ

  Calculate infrastructure costs, deployment velocity, and risk mitigation for AI feature flags.

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• 

  Model Routing Efficiency Calculator AIコネクタ

  Optimize LLM selection by analyzing cost-quality trade-offs and task complexity.

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