LLM Rouge Bleu EvaluatorをChatGPTとClaudeに接続

• 01 / 03プロンプトの精度を比較する
• 02 / 03RAGの検証方法
• 03 / 03AI要約の精度を評価する

最速日

608ms

最遅日

1055ms

14日間の傾向

安定-4%

Native V8 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_z0q7gwUAfzM0N3ZK3eZQCz4jX1vB3r37FG9LvR3T/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 — Native V8 capabilities are ready to use.

設定 · claude_desktop_config.jsonコピー

{
  "mcpServers": {
    "llm-rouge-bleu-evaluator-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_z0q7gwUAfzM0N3ZK3eZQCz4jX1vB3r37FG9LvR3T/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？ Native V8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/機械学習エンジニア

  LLMのファインチューニングやRAGシステムの性能評価を行うプロフェッショナル。

• 02

  NLP研究者

  自然言語処理モデルの出力精度を、学術的な標準指標を用いて検証する専門家。

• 03

  データサイエンティスト

  生成AIの出力がビジネス要件を満たしているかを、決定論的な指標で測定する担当者。

• 01

  Why not just ask the LLM to grade the text?

  LLMs are probabilistic and prone to hallucinating scores. This MCP uses deterministic math to ensure the scores are accurate and repeatable.

• 02

  What is the difference between BLEU and ROUGE?

  BLEU focuses on precision, while ROUGE focuses on recall. This MCP provides both to give a full picture of text overlap.

• 03

  Which AI clients can use this MCP?

  You can use this with any MCP-compatible client, including Claude, Cursor, and Windsurf.

• 04

  Do I need to host the math engine myself?

  No. Vinkius hosts the MCP and manages everything. You just connect and use the capabilities.

• 05

  Can I use this for RAG evaluation?

  Yes. It is specifically useful for checking how well your RAG system's output matches your ground truth documents.

• 06

  What does BLEU measure?

  BLEU (Bilingual Evaluation Understudy) measures precision: how many of the words generated by the AI actually appeared in the human reference text.

• 07

  What does ROUGE measure?

  ROUGE measures recall: how much of the original human reference text was successfully captured and reproduced by the AI's generated summary.

• 08

  Can it evaluate RAG prompts?

  Yes! By keeping your expected answer as the reference, you can automatically score how well your RAG pipeline retrieved and generated the facts.

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