Amazon Bedrock KB MCP Server
Connect your AI agent to AWS Bedrock Knowledge Bases — execute semantic searches, managed RAG, and sync vector datasources natively.
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What is the Amazon Bedrock MCP Server?
The Amazon Bedrock MCP Server gives AI agents like Claude, ChatGPT, and Cursor direct access to Amazon Bedrock via 6 tools. Connect your AI agent to AWS Bedrock Knowledge Bases — execute semantic searches, managed RAG, and sync vector datasources natively. Powered by the Vinkius - no API keys, no infrastructure, connect in under 2 minutes.
Built-in capabilities (6)
Tools for your AI Agents to operate Amazon Bedrock
Ask your AI agent "Which knowledge bases and embedding models do I have setup?" and get the answer without opening a single dashboard. With 6 tools connected to real Amazon Bedrock data, your agents reason over live information, cross-reference it with other MCP servers, and deliver insights you would spend hours assembling manually.
Works with Claude, ChatGPT, Cursor, and any MCP-compatible client. Powered by the Vinkius - your credentials never touch the AI model, every request is auditable. Connect in under two minutes.
Why teams choose Vinkius
One subscription gives you access to thousands of MCP servers - and you can deploy your own to the Vinkius Edge. Your AI agents only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure and security, zero maintenance.
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Amazon Bedrock KB MCP Server capabilities
6 toolsGet an explicit AWS Bedrock knowledge base
List Data Sources bound explicitly to an AWS Bedrock KB
List AWS Bedrock KB explicit sync operations
List AWS Bedrock knowledge bases
Query a vector index securely via AWS Bedrock
Generate explicitly grounded LLM responses using Bedrock KB
What the Amazon Bedrock KB MCP Server unlocks
Connect your Amazon Bedrock account to any AI agent and empower it with managed vector databases, enterprise RAG workflows, and semantic search directly inside AWS.
What you can do
- Managed RAG — Generate grounded LLM responses using internal document sets in a single explicit call
- Semantic Retrieval — Query vector indexes to retrieve exact top-K text chunks and their origin document URLs
- Data Sources — Inspect and paginate attached storage buckets feeding the knowledge base
- Ingestion Jobs — Track real-time syncing status of chunking pipelines mapping documents across the vector layout
- Knowledge Base Introspection — List available vector stores and exact embedding models assigned directly to your region
How it works
1. Subscribe to this server
2. Enter your AWS IAM Role/User Access Credentials
3. Start augmenting your agent's context from Claude, Cursor, or any MCP-compatible client
Eliminate the need to build custom vector pipelines. Your agent queries massive corporate datasets precisely where they reside in AWS.
Who is this for?
- AI Developers — build RAG workflows rapidly without hosting databases or maintaining chunking sync logic
- Cloud Architects — audit ingestion status and check origin document mappings securely from your chat interface
- Data Scientists — prototype context-grounded queries instantly and trace accuracy against exact data chunks
Frequently asked questions about the Amazon Bedrock KB MCP Server
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.
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Give your AI agents the power of Amazon Bedrock MCP Server
Production-grade Amazon Bedrock KB MCP Server. Verified, monitored, and maintained by Vinkius. Ready for your AI agents — connect and start using immediately.






