Master Amazon Bedrock Knowledge Base Retrieval MCP Server with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Ensure you have Python
10+ installed and AWS CLI configured with appropriate credentials and profile Set up Amazon Bedrock Knowledge Base in your AWS account and tag with 'mcp
kb=true' for server discovery Install the MCP server using 'uvx awslabs.bedrock
server@latest' and configure your AI assistant (Kiro, Cursor, VS Code, Claude Desktop) to use the server Configure environment variables including AWS_PROFILE, AWS_REGION, and optional settings like KB_INCLUSION_TAG_KEY for filtering Test the integration by querying your knowledge base through your configured AI assistant and verify citation support is working correctly
💡 Quick Start: Follow these 4 steps in order to get up and running with Amazon Bedrock Knowledge Base Retrieval MCP Server quickly.
The Model Context Protocol (MCP) is an open standard developed by Anthropic for connecting AI assistants to external data sources. It has been adopted by major AI platforms including OpenAI, Google DeepMind, Microsoft, and thousands of developers. MCP provides a standardized way for AI assistants to access real-world data and tools, eliminating the need for custom integrations. This means you configure the server once and it works across Kiro, Cursor, VS Code, Claude Desktop, and other MCP-compatible tools without building separate plugins for each.
Yes, you must have at least one Amazon Bedrock Knowledge Base already set up and configured in your AWS account. The MCP server connects to existing knowledge bases rather than creating new ones. Your knowledge base must be tagged with the key 'mcp-multirag-kb' set to a value of 'true' for the server to discover and access it. You can also use the KB_INCLUSION_TAG_KEY environment variable to specify a custom tag key for filtering which knowledge bases are exposed to the server.
Installation uses Python's uv package manager. First install uv from Astral, then install Python 3.10 via 'uv python install 3.10'. Configure the server in your AI assistant's MCP settings file by pointing to the 'awslabs.bedrock-kb-retrieval-mcp-server@latest' package via uvx. You'll need to set environment variables for AWS_PROFILE, AWS_REGION, and optionally BEDROCK_KB_RERANKING_ENABLED. Docker-based installation is also supported for containerized environments, though you'll need to manage AWS credential refresh on the host.
The MCP server software is completely free and open source. However, you will incur AWS service costs including Amazon Bedrock Knowledge Base query charges, vector database costs (OpenSearch Serverless, etc.), S3 storage costs for your data sources, and optional reranking model inference costs when that feature is enabled. Costs scale with usage volume, so light development use may be minimal while heavy production querying across large knowledge bases will increase proportionally.
Reranking uses Amazon Bedrock's foundation models to re-score and reorder retrieval results by relevance to your query, improving the quality of information surfaced to your AI assistant. It is disabled by default (BEDROCK_KB_RERANKING_ENABLED=false) because it requires additional IAM permissions for bedrock:Rerank and bedrock:InvokeModel actions, model access enablement in your region, and incurs extra inference costs. Enable it when result quality is critical—such as querying complex technical documentation—and disable it for cost-sensitive or latency-sensitive use cases. Individual API calls can override the global setting.
Now that you know how to use Amazon Bedrock Knowledge Base Retrieval MCP Server, it's time to put this knowledge into practice.
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Tutorial updated March 2026