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Connect Amazon Bedrock Kb to ChatGPT, Claude and Gemini

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Ground your AI responses directly in your AWS data.

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

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

A futuristic MCP client docking bay
V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 6 capabilities

We ship only the actions that matter.

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

Real work

What teams actually run

01

Internal Onboarding

Run a retrieval query to pull the top three relevant policy snippets for a new employee checklist, citing the exact source document.

02

Compliance Auditing

List all data sources and check their origin document mappings to ensure sensitive data is properly included in the knowledge base.

03

Knowledge Base Setup

List all available knowledge bases and check which embedding models are assigned to your region before starting a new project.

04

Data Pipeline Monitoring

Check the ingestion job status to confirm that new documentation buckets have finished syncing and mapping without errors.

Automate a task

Put Amazon Bedrock to work on one thing.

Step-by-step automations your AI runs through this Connector: the workflow, the prompts to start with, and the exact capabilities behind each move.

One connector, every AI

Amazon Bedrock works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

Building your own app? The connector is yours to use.

You don't need a client to put Amazon Bedrock to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

925ms average. Fast in production.

Amazon Bedrock is checked daily against the live service.

Daily averagePeak 1140ms
Oct 3Today
Fastest day
723ms
Slowest day
1140ms
14-day trend
Slowing+17%

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

Guided setup for Claude? Set up Amazon Bedrock in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Amazon Bedrock owners hand off.

This MCP is built for technical teams that need to connect AI agents to secure, enterprise-level data within AWS. It lets developers prototype complex RAG workflows and allows architects to audit data flow without building custom infrastructure.

  • 01

    AI Developers

    Build RAG workflows quickly without having to host databases or manage chunking synchronization logic.

  • 02

    Cloud Architects

    Audit ingestion status and check the origin document mappings securely directly from your chat interface.

  • 03

    Data Scientists

    Prototype context-grounded queries instantly and trace accuracy against exact data chunks.

FAQ

Questions Amazon Bedrock owners ask.

  • 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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