Amazon Bedrock KB MCP, Ready to Go
Connect your AI agents to AWS Bedrock Knowledge Bases. Use Claude or Cursor to query private data and manage RAG workflows natively.
No credit card required. Experience the power of this integration risk-free.
Query private AWS data and manage managed RAG workflows through your chat interface.
Works with every AI agent you already use
…and any MCP-compatible client
How fast is the Amazon Bedrock KB Connector?
Average time for the server to become ready for requests over the last 14 days, measured until the initialize / tools/list handshake completes. Metrics are updated daily between 00:00 and 04:00 UTC. Create a free account, use this Connector on Vinkius Cloud, and connect it to your AI agent in seconds.
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What AI agents can do with Amazon Bedrock KB 6-Tool AWS RAG Connector
Query vector indexes, check ingestion status, and manage AWS Bedrock Knowledge Bases via your agent.
List knowledge bases
This tool lists all your AWS Bedrock knowledge bases. It lets you see your entire collection in one view.
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.
Retrieve
This tool queries your vector index to find relevant text chunks. It provides the exact content and the source URL for each result.
Retrieve and generate
Use this to get a full answer grounded in your private documents. It combines retrieval and generation into one simple step.
List data sources
This tool shows you the storage buckets bound to a knowledge base. It helps you audit exactly where your data is coming from.
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.
A Connector is a URL. Vinkius runs it: hosting, security, governance, observability.
You're looking at one of 5,800+ managed Connectors. The real value isn't the catalog. It's the control plane that secures, governs, audits, and manages every interaction between your agents and the tools they use.
No Shadow AI
Every agent action is visible, approved, and auditable. Nothing runs outside your governance.
Absolute agent control
Fine-grained permissions for every agent, MCP, and tool. Instantly revoke access and audit every execution.
Cost control per token
Spend broken down to the token, tool, and agent. Budgets and hard limits. No surprise invoices.
Managed & monitored infra
We operate the runtime, authentication, scaling, retries, and monitoring. Your team manages AI, not infrastructure.
Data protection, DLP by design
Sensitive data is filtered before reaching the model. Access is governed so agents receive only the information they're allowed to use.
Token optimization, real savings
Lower AI costs by delivering the right context instead of unnecessary tools. Better accuracy, faster responses, and fewer wasted tokens.
Amazon Bedrock KB for Enterprise RAG and AWS Data Retrieval
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.
Frequently Asked Questions
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.
Your AI, connected to everything.
No credit card required · Free tier available
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