AI Portfolio Economics OptimizerをAIに接続

最速日

710ms

最遅日

1377ms

14日間の傾向

遅延傾向+37%

AI Portfolio Economics 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_XzAEDVOVYaCSnEL7ACyI8wrokOIjK3bIH83SxaU0/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 Portfolio Economics capabilities are ready to use.

設定 · claude_desktop_config.jsonコピー

{
  "mcpServers": {
    "ai-portfolio-economics-optimizer-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_XzAEDVOVYaCSnEL7ACyI8wrokOIjK3bIH83SxaU0/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 Portfolio Economics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

  AIアーキテクト

  複数のAIモデルを組み合わせて最適なシステム設計を行う際、コスト効率の高いモデル比率を決定できます。

• 02

  機械学習エンジニア

  実際のワークロードデータに基づき、性能要件を満たすための最適なモデルミックスを計算し、実装の指針を得られます。

• 03

  プロダクトマネージャー

  AI機能のコスト対効果を定量的に把握し、ビジネス目標達成に向けた投資判断を下すことができます。

• 01

  Does this MCP work with all LLMs and SLMs?

  It is designed to optimize model portfolios generally. You provide the necessary capabilities data, and the MCP calculates the optimal mix based on those inputs.

• 02

  What kind of data do I need to provide?

  You need to provide detailed information about your workload, including the percentage distribution of different task types and the required accuracy thresholds for those tasks.

• 03

  Is this just a theoretical cost estimate?

  No, the MCP provides concrete, quantifiable reports. You can get a cost savings report that directly compares your optimized setup against a pure LLM baseline.

• 04

  Can I use this if I don't know my performance requirements?

  The MCP helps you define those requirements. You can use the tradeoff matrix to visualize the impact of changing performance targets on your overall cost.

• 05

  How does the optimizer account for routing errors?

  The calculate_optimal_mix capability uses the routingAccuracy parameter to adjust expected performance, ensuring that even if a query is misrouted to a less capable model, the overall performance remains above your defined threshold.

• 06

  Can I compare my current setup to an optimized one?

  Yes, you can use get_cost_savings_report to compare the cost of your optimized portfolio against a baseline where only LLMs are used for every task.

• 07

  How do I know if my model mix is actually viable?

  You can use validate_portfolio_feasibility to check if a specific proposed allocation of LLMs and SLMs will meet your required performance levels given your current routing accuracy.

• 

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  Context Window Economics Optimizer AIコネクタ

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