Distance Metrics EngineをChatGPTとClaudeに接続

• 01 / 02ベクトル類似度を計算する
• 02 / 02異常値の検知方法

最速日

653ms

最遅日

1110ms

14日間の傾向

改善傾向-17%

Distance Metrics 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_y2uLWGNkX2DGyvuukg2OciAQezxFmCM92SNAHYKa/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 — Distance Metrics Engine capabilities are ready to use.

設定 · claude_desktop_config.jsonコピー

{
  "mcpServers": {
    "distance-metrics-engine-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_y2uLWGNkX2DGyvuukg2OciAQezxFmCM92SNAHYKa/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？ Distance Metrics 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

  Machine Learning Engineer

  Hands off vector similarity tasks to the MCP to ensure model evaluation is accurate.

• 02

  AI Application Developer

  Uses the capability to build RAG systems that require precise document retrieval.

• 03

  Data Scientist

  Delegates high-dimensional distance calculations to the agent for rapid prototyping.

• 01

  Why can't my AI just calculate these distances itself?

  LLMs are probabilistic and tend to approximate math. This leads to errors in high-dimensional spaces where precision is required.

• 02

  Does this MCP work with OpenAI embeddings?

  Yes, it is designed to handle 1536-dimensional vectors commonly used in those embeddings.

• 03

  Is my data safe when using this capability?

  Yes, the math is performed locally, so your embedding vectors and model weights do not leave your machine.

• 04

  Which distance metrics are supported?

  The MCP supports Cosine, Euclidean, Manhattan, and Chebyshev distances.

• 05

  Which AI clients can use this MCP?

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

• 06

  Is Cosine distance the same as Cosine similarity?

  No, Cosine Distance equals 1 minus Cosine Similarity. The engine returns both exact values in the JSON response so you always have the complete picture.

• 07

  Can it compare 1536-dimensional embeddings like OpenAI's?

  Yes! It processes any equal-length array instantly. 1536-dimensional vectors are evaluated in milliseconds local, with exact floating-point precision.

• 08

  What if the two vectors have different lengths?

  The engine enforces a strict validation constraint and throws a clear error. Both arrays must be mathematically equal in length. there is no silent truncation or padding.

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