Agent Scoring Ranking EngineをAIに接続

最速日

961ms

最遅日

1094ms

14日間の傾向

改善傾向-12%

Agent Scoring Engine 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_pxy20XdHnsTrdCZB8wc0Ck1kM4p7yyqCZvVt0KgF/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 — Agent Scoring Engine capabilities are ready to use.

設定 · claude_desktop_config.jsonコピー

{
  "mcpServers": {
    "agent-scoring-ranking-engine-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_pxy20XdHnsTrdCZB8wc0Ck1kM4p7yyqCZvVt0KgF/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？ Agent Scoring Engine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

  データサイエンティスト

  複数のメトリクスを統合し、統計的に信頼性の高い性能スコアを算出する際に利用します。

• 02

  AIエンジニア

  自律エージェントのバージョンアップや比較において、最適なトレードオフ点（パレート最適点）を見つけ出すために使用します。

• 03

  プロダクトマネージャー

  エージェントのパフォーマンスを定量的に評価し、どの機能改善に最も注力すべきかという優先順位付けの根拠を得るために利用します。

• 01

  Does this MCP handle different types of metrics?

  Yes. It is designed to process diverse metrics like accuracy (a percentage), latency (milliseconds), and cost (currency). It normalizes them so you can compare them fairly in a single score.

• 02

  What is the difference between scoring and ranking?

  Scoring gives you a single, quantitative number for an agent's performance. Ranking takes those scores and puts them into a clear, ordered list, showing you the top performers.

• 03

  Can I prevent one metric from dominating the score?

  Absolutely. You use the capability to adjust weights for correlation, which prevents any single metric from unfairly skewing the final composite score.

• 04

  What is the Pareto frontier?

  It's a mathematical concept that finds the optimal trade-off. It shows you the set of agents where you can't improve one metric (like speed) without sacrificing another (like accuracy).

• 05

  Is this MCP suitable for real-time scoring?

  The MCP provides stable, reproducible rankings, making it ideal for batch evaluation workflows. The average latency is low, supporting frequent scoring runs.

• 06

  How are agent scores calculated?

  Scores are calculated by applying min-max normalization to each metric and multiplying the result by its assigned weight. For metrics where lower is better, like latency, the normalization is inverted.

• 07

  Can I adjust weights if metrics are correlated?

  Yes, you can use adjust_weights_for_correlation to detect statistical dependencies and receive suggested weight adjustments to prevent double-counting performance traits.

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