Amazon Bedrock KbをChatGPT、Claude、Geminiに接続

• 01 / 03ナレッジベースの監査方法
• 02 / 03AWSデータを活用する
• 03 / 03同期状況の確認方法

最速日

723ms

最遅日

1140ms

14日間の傾向

遅延傾向+17%

Amazon Bedrock 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_VAqgKgPAMaCVADpiaKW1YIUR9Y3n0l8xHvlUnkn1/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 — Amazon Bedrock capabilities are ready to use.

設定 · claude_desktop_config.jsonコピー

{
  "mcpServers": {
    "amazon-bedrock-kb-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_VAqgKgPAMaCVADpiaKW1YIUR9Y3n0l8xHvlUnkn1/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？ Amazon Bedrock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開発者

  データベースのホスティングやチャンキング同期ロジックの維持管理なしに、RAGワークフローを迅速に構築できます。

• 02

  クラウドアーキテクト

  チャットインターフェースから、取り込みのステータスを監査したり、出典ドキュメントのマッピングを確認したりできます。

• 03

  データサイエンティスト

  コンテキストに基づいたクエリのプロトタイピングを即座に行い、正確なデータチャンクと比較検証できます。

• 01

  Do I need to manage my own vector database?

  No. This MCP connects directly to AWS Bedrock, giving you managed vector databases and eliminating the need for you to build or host custom vector pipelines.

• 02

  What kind of data can I query?

  You can query massive corporate datasets stored in attached storage buckets, such as S3. The MCP handles the chunking and mapping of these documents into the knowledge base.

• 03

  How do I check if my data is syncing correctly?

  You can use the MCP to list ingestion jobs and track the real-time syncing status of chunking pipelines, ensuring your documents are mapped to the index.

• 04

  Is this only for AWS users?

  Yes. This MCP is designed specifically to connect your AI agent to the native services and infrastructure within your Amazon Bedrock account.

• 05

  What is the difference between 'retrieve' and 'retrieve_and_generate'?

  The 'retrieve' capability pulls the raw, top-K text chunks from the vector index. The 'retrieve_and_generate' capability uses those chunks to generate a fully grounded, synthesized response from the LLM.

• 06

  Can my AI agent directly run RAG without calling external LLMs?

  Yes! Use the retrieve_and_generate capability. Your agent passes the query and a designated Bedrock model ARN. Bedrock handles fetching chunks from the local vector index and synthesizing the final answer inside AWS boundaries, returning a fully grounded response instantly.

• 07

  How can I check if new uploaded documents are successfully indexed in my agent?

  Just ask your agent to list ingestion jobs for a specific Knowledge Base ID and Data Source ID. It will report back the exact status (e.g., SYNCING, COMPLETED, FAILED) of chunks being mapped to your vector layout.

• 08

  Can I see exactly where an answer came from in my documentation?

  Absolutely. Both the standard retrieve functionality and retrieve_and_generate calls will parse out the specific origin document URLs (e.g., S3 paths) and expose the exact raw text snippets that mathematically matched your query vector.

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