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ClaudeChatGPTPerplexityGeminiMicrosoft CopilotRaycastMeta AIGrokZ.aiQwenKimi
DeepSeekMistralCursorVS CodeWindsurfJetBrainsClineLovableVercel AI SDKLangChain

Use Amazon Bedrock with your AI.

Connect your account once and let the AI you already use work with it, without building another integration. Connect your AI agent to AWS Bedrock Knowledge Bases. execute semantic searches, managed RAG, and sync vector datasources natively.

Included with plan

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Complete set · 6 capabilities

The complete Amazon Bedrock capability set.

These are the exact actions your AI can choose when you ask it to work with Amazon Bedrock.

Capability set01 / 02

01-03

3 capabilities in this set.

Part of 6 available through Amazon Bedrock.

  1. 01

    List ingestion jobs

    List AWS Bedrock KB explicit sync operations

  2. 02

    Get knowledge base

    Get an explicit AWS Bedrock knowledge base

  3. 03

    List data sources

    List Data Sources bound explicitly to an AWS Bedrock KB

Capability set02 / 02

04-06

3 capabilities in this set.

Part of 6 available through Amazon Bedrock.

  1. 04

    List knowledge bases

    List AWS Bedrock knowledge bases

  2. 05

    Retrieve

    Query a vector index securely via AWS Bedrock

  3. 06

    Retrieve and generate

    Generate explicitly grounded LLM responses using Bedrock KB

Observed, not estimated

794ms average. Fast in production.

Amazon Bedrock is checked daily against the live service.

Daily averagePeak 1070ms
Aug 24Today
Fastest day
725ms
Slowest day
1070ms
14-day trend
Improving-11%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 6 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Amazon Bedrock, so you can see the experience inside your AI.

It does not authenticate your account with Amazon Bedrock. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Amazon Bedrock Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_VAqgKgPAMaCVADpiaKW1YIUR9Y3n0l8xHvlUnkn1/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  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.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "amazon-bedrock-kb-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_VAqgKgPAMaCVADpiaKW1YIUR9Y3n0l8xHvlUnkn1/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

FAQ

Questions Amazon Bedrock owners ask.

  • 01

    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.

  • 02

    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.

  • 03

    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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