Amazon Bedrock KB Connector for AI agents.
6 live capabilities
Query private AWS data and manage managed RAG workflows through your chat interface.
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Why people use Amazon Bedrock KB
Amazon Bedrock KB for Enterprise RAG and AWS Data Retrieval
This Connector changes that by putting those AWS controls right into your chat. You can query your vector stores, check sync statuses, and pull grounded answers without leaving your agent's interface. You get a direct line to your data.
What Vinkius changes
You get production-grade RAG capabilities in your chat interface without managing any vector infrastructure.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Finding HR policies in large PDFs
An HR manager asks the agent for the remote work policy.
- Real-world use case 02
Technical documentation Q&A
A developer asks if a specific API key is in the docs.
- Real-world use case 03
Sync status monitoring
A cloud admin needs to know if the latest product manual finished syncing.
Complete set · 6capabilities
The complete Amazon Bedrock KB capability set.
These are the exact actions your AI can choose when you ask it to work with Amazon Bedrock KB.
01—03
3 capabilities in this set.
Part of 6 available through Amazon Bedrock KB.
- 01 Capability
List data sources
This capability shows you the storage buckets bound to a knowledge base. It helps you audit exactly where your data is coming from.
- 02 Capability
List ingestion jobs
Use this to see the status of your data syncing operations. It tells you if your documents were successfully chunked and mapped.
- 03 Capability
Get knowledge base
Use this to pull the specific details of a single knowledge base. It's the best way to see individual IDs and settings.
04—06
3 capabilities in this set.
Part of 6 available through Amazon Bedrock KB.
- 04 Capability
List knowledge bases
This capability lists all your AWS Bedrock knowledge bases. It lets you see your entire collection in one view.
- 05 Capability
Retrieve
This capability queries your vector index to find relevant text chunks. It provides the exact content and the source URL for each result.
- 06 Capability
Retrieve and generate
Use this to get a full answer grounded in your private documents. It combines retrieval and generation into one simple step.
Set up in minutes
One URL. Then ask Amazon Bedrock KB to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Amazon Bedrock KB from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_VAqgKgPAMaCVADpiaKW1YIUR9Y3n0l8xHvlUnkn1/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Amazon Bedrock KB, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Amazon Bedrock KB for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_VAqgKgPAMaCVADpiaKW1YIUR9Y3n0l8xHvlUnkn1/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Amazon Bedrock KB URL.
- Step 03
Save and start
Save the connection and enable Amazon Bedrock KB in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"amazon-bedrock-kb": {
"url": "https://edge.vinkius.com/vk_preview_VAqgKgPAMaCVADpiaKW1YIUR9Y3n0l8xHvlUnkn1/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Amazon Bedrock KB
Open Agent mode in chat and ask: "Using Amazon Bedrock KB, help me...". 6 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"amazon-bedrock-kb": {
"url": "https://edge.vinkius.com/vk_preview_VAqgKgPAMaCVADpiaKW1YIUR9Y3n0l8xHvlUnkn1/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Amazon Bedrock KB
Ask Copilot: "Using Amazon Bedrock KB, help me...". 6 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"amazon-bedrock-kb": {
"url": "https://edge.vinkius.com/vk_preview_VAqgKgPAMaCVADpiaKW1YIUR9Y3n0l8xHvlUnkn1/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Amazon Bedrock KB
Open Cascade and ask: "Using Amazon Bedrock KB, help me...". 6 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"amazon-bedrock-kb": {
"url": "https://edge.vinkius.com/vk_preview_VAqgKgPAMaCVADpiaKW1YIUR9Y3n0l8xHvlUnkn1/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Amazon Bedrock KB
Ask Cline: "Using Amazon Bedrock KB, help me...". 6 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add amazon-bedrock-kb --transport http "https://edge.vinkius.com/vk_preview_VAqgKgPAMaCVADpiaKW1YIUR9Y3n0l8xHvlUnkn1/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Amazon Bedrock KB
Ask Claude: "Using Amazon Bedrock KB, show me...". 6 tools are ready
Where the request belongs
Work Amazon Bedrock can move forward.
This is for the engineers and architects who need to put AI to work on private data without the headache of building and maintaining a custom RAG stack from scratch.
AI Developer
Building RAG apps quickly without worrying about chunking logic or database maintenance.
Cloud Architect
Auditing data ingestion and checking document mappings across AWS regions.
Data Scientist
Prototyping context-grounded queries and checking accuracy against raw data chunks.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsR2R
Equip your AI with direct access to your R2R engine. execute vector searches, run precise RAG queries, and manage your documents.
Cognita (RAG Framework)
Manage modular RAG via Cognita. list collections, ingest data sources, and perform AI-driven Q&A directly from any AI agent.
Verba
Connect your Verba RAG platform to your AI agent. Search your documents, retrieve semantic answers, and manage your Weaviate knowledge base directly.
Linkup (AI Search & RAG)
Power your AI agents with real-time web search via Linkup. execute semantic queries and extract RAG-ready content.
FastGPT
Manage FastGPT Knowledge Bases. automate dataset creation, document ingestion, and RAG search directly from any AI agent.
GroundX
Data search and RAG optimization platform.
Bring your own AI
Change the model, client or framework. Keep Amazon Bedrock connected.
-
Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Amazon Bedrock.
The practical details behind the request, access and result.
Can the Amazon Bedrock KB MCP access my private S3 files?
Yes, it connects to the Knowledge Bases you've already set up in AWS. If your S3 bucket is already linked as a data source for a knowledge base, your agent can query it.
Does this Connector help with RAG?
Yes, it is specifically designed for managed RAG workflows. It allows your agent to retrieve relevant context from your AWS data to generate grounded answers.
Can I use this with Claude or Cursor?
Yes, it works with any MCP-compatible client. You can use it to bring your AWS data into Claude, Cursor, Windsurf, or any other compatible agent.
Do I need to move my data to a new database?
No, you don't. This Connector works with the Knowledge Bases you already have in AWS. You don't need to migrate your data to a third-party vector database.
How do I check if my documents are synced?
You can simply ask your agent to check your ingestion jobs. It will look up the real-time status of your chunking pipelines and tell you if the sync is finished.
Is my data secure with this Connector?
Yes, your data stays within your AWS environment. The Connector simply provides a way for your agent to interact with the services you've already authorized in your AWS account.
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.
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.
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.
One connection away
Give your agent a direct line to Amazon Bedrock.
Connect Amazon Bedrock once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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