Retrieval Relevance ScorerをAIに接続

最速日

753ms

最遅日

936ms

14日間の傾向

遅延傾向+24%

Retrieval Relevance Scorer 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_Kx7cXouaqZrfTsnawEqwgyW4QrEjd9p5Y4I1xFB8/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 — Retrieval Relevance Scorer capabilities are ready to use.

設定 · claude_desktop_config.jsonコピー

{
  "mcpServers": {
    "retrieval-relevance-scorer-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_Kx7cXouaqZrfTsnawEqwgyW4QrEjd9p5Y4I1xFB8/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？ Retrieval Relevance Scorer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エンジニア

  RAGの検索パイプラインにおけるハルシネーションを抑制したい開発者。

• 02

  データサイエンティスト

  数学的な指標を用いて検索ドキュメントの関連性を厳密に評価したい専門家。

• 03

  LLMアプリケーション開発者

  AIクライアントに渡すコンテキストの質を向上させ、回答精度を安定させたい担当者。

• 01

  How does this MCP reduce hallucinations?

  It prevents the AI from seeing irrelevant or noisy text that might confuse its reasoning. By filtering documents using deterministic math, only high-signal context reaches the prompt.

• 02

  What metrics does the scoring engine use?

  The engine uses Jaccard Similarity for keyword overlap, TF-IDF Cosine Similarity for term importance, and Query Term Coverage for information density.

• 03

  Can I see how the scores are calculated?

  Yes, you can use the get_scoring_config capability to inspect the current weight distributions and operational parameters.

• 04

  Which AI clients can use this MCP?

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

• 05

  How do I find out if my documents are missing information?

  Use the analyze_coverage_gap capability to identify which specific terms from your query are not present in the document set.

• 06

  How does the scoring work?

  The engine calculates a composite score by combining Jaccard similarity, TF-IDF cosine similarity, and query term coverage using configurable weights.

• 07

  Can I customize the weights?

  Yes, you can pass a custom weights object to the score_documents capability to prioritize different metrics like keyword overlap or TF-IDF.

• 08

  How do I diagnose why a document was filtered out?

  You can use the analyze_coverage_gap capability to identify which specific terms from your query are missing from the retrieved documents.

• 

  rag-chunk-boundary-optimizer AIコネクタ

  Analyzes text chunking strategies for RAG pipelines by measuring overlap, sentence integrity, and semantic con

  表示
• 

  RAG Chunk Boundary Optimizer AIコネクタ

  Analyze text chunking integrity in RAG pipelines by measuring overlap, sentence breaks, and semantic continuit

  表示
• 

  RAG Chunk Selection Optimizer AIコネクタ

  A deterministic engine to select the most effective RAG information chunks within token budgets.

  表示
• 

  BM25 Context Relevance Scorer AIコネクタ

  Deterministic BM25 relevance scoring engine for RAG optimization.

  表示

おすすめ

• 

  Cognita (RAG Framework) AIコネクタ

  Manage modular RAG via Cognita — list collections, ingest data sources, and perform AI-driven Q&A directly fro

  表示
• 

  Linkup (AI Search & RAG) AIコネクタ

  Power your AI agents with real-time web search via Linkup — execute semantic queries and extract RAG-ready con

  表示
• 

  Deterministic Readability Scorer AIコネクタ

  Equip your AI with strict linguistic math. Calculate Flesch-Kincaid, Gunning Fog indexes, and exact reading ti

  表示
• 

  Deterministic EdTech Quiz Scorer AIコネクタ

  Transform your AI into a ruthless, high-precision EdTech examiner. Automatically cross-reference quiz answers

  表示